Systems, methods, and apparatuses for positive progression of inspection operations

Abstract

A device may include a data collection component configured to interpret inspection data related to an asset collected from a sensor. A device may include a data processing unit configured for analyzing the inspection data at an asset level and at a fleet level. A device may include an action module configured for determining at least one action in response to the analysis based on a plurality of parameters. A device may include an asset feedback module configured to adjust at least one parameter of the action module based on outcomes of previously determined actions. A device may include a fleet feedback module configured to adjust at least one parameter of the action module based on historical data.

Description

PATENT Attorney Docket No. GROB-0028-WO SYSTEMS, METHODS, AND APPARATUSES FOR POSITIVE PROGRESSION OF INSPECTION OPERATIONS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of U.S. Provisional Patent App. No. 63 / 746,507, filed on 17 JAN 2025, and entitled “SYSTEMS, METHODS, AND APPARATUSES FOR POSITIVE PROGRESSION OF INSPECTION OPERATIONS” (GROB-0028-P01).

[0002] The present application claims the benefit of U.S. Provisional Patent App. No. 63 / 796,780, filed on 29 APR 2025, and entitled “SYSTEM FOR ASSET AND FACILITY PLANNING USING NONDESTRUCTIVE TESTING DATA” (GROB-0036-P01).

[0003] Each of the foregoing applications is incorporated herein by reference in the entirety for all purposes.SUMMARY

[0004] Example benefits of the present disclosure include transforming siloed inspection data into fleet-aware decisions by integrating analytics and operational context, as evidenced by language describing a data processing unit configured for analyzing the inspection data at an asset level and at a fleet level, the ability to benchmark asset performance against similar assets within the fleet to prioritize actions across multiple assets, and integration to refine the asset-level and facility -level analysis using external operational systems.

[0005] Example benefits of the present disclosure include improving uptime and reducing unplanned failures through predictive analytics that detect early-stage anomalies and forecast degradation, as shown by capabilities to predict future asset failures based on a combination of historical data and real-time sensor data, employ predictive analytics configured to detect early-stage anomalies that could lead to asset failure, and use machine learning to analyze real-time operational data; compare predicted outcomes against actual performance; refine prediction models based on observed deviations; and adjust scenario parameters based on learned patterns.

[0006] Example benefits of the present disclosure include optimizing inspection cadence and sensor utilization by dynamically adjusting data collection based on value and risk, including operations to dynamically modify data collection frequency based on at least one of asset criticality or detected anomalies, to modify the type of sensor data collected based on outcomes of previous actions, and to dynamically adjust maintenance schedules based on real-time asset conditions and fleet-wide performance indicators, aligning inspection intensity with operational needs.

[0007] Example benefits of the present disclosure include increasing lifecycle value via action selection that balances repair, replacement, maintenance, and enhanced monitoring with cost, safety, and operational impact, by enabling selection of actions from repair, replacement, maintenance, orPATENT Attorney Docket No. GROB-0028-WO inspection of the asset, requiring a validation operation to compare potential outcomes of determined actions against at least one of predefined safety standards or predefined operational standards, and implementing cost-benefit analysis to prioritize repairs that maximize operational lifecycle value.

[0008] Example benefits of the present disclosure include shortening time-to-value for new assets by applying transfer learning and benchmarking from similar assets and operating conditions, including features to apply historical data from similar assets to iteratively improve parameters for newly added assets in the system, to benchmark asset performance against similar assets within the fleet, and to employ algorithms that refine prediction models based on observed deviations and adapt scenario parameters as evidence accumulates.

[0009] Example benefits of the present disclosure include enhancing decision confidence with closed-loop feedback that validates outcomes and continuously refines models and thresholds at asset and fleet levels, through modules where an asset feedback module and a fleet feedback module each adjust at least one parameter of the action module based on observed results, coupled with steps of evaluating an outcome of the action based on updated sensor data and adjusting future asset management based on the outcome.

[0010] Example benefits of the present disclosure include streamlining field operations through a field-accessible interface enabling rapid extension creation, deployment, validation, and offline continuity with synchronization, by providing a field-accessible interface for accessing and modifying system extensions, incorporating field validation tools configured to analyze extension performance in real-time with metrics such as response times, accuracy, and resource utilization, and supporting offline operation that automatically synchronizes extension modifications and operational data when internet connectivity is restored.

[0011] Example benefits of the present disclosure include strengthening governance and compliance using a multidimensional permission system combining role-, action-, and asset-specific controls with auditability, by implementing a role-based permissions management module, an action-based permissions module, and an asset-specific permissions module coordinated by a multidimensional permissions matrix, while also allowing graphical permission relationship displays; direct matrix manipulation capabilities; permission conflict identification; and modification audit logging to maintain oversight.

[0012] Example benefits of the present disclosure include accelerating repair planning and execution by auto-identifying repair areas, simulating alternatives, and generating standards-compliant instructions with real-time adjustments, through a platform that includes a repair planning module, a repair simulation module, and an instruction generation module within a feedback loop, incorporates industry-specific standards comprising API standards and regulatory requirements, and automaticallyPATENT Attorney Docket No. GROB-OQ28-WO modifies repair instructions in response to real-time inspection data indicating changes in asset condition during repair implementation.

[0013] Example benefits of the present disclosure include reducing total cost of ownership via costbenefit prioritization across inspections, repairs, maintenance, and replacements aligned with operational goals, by enabling cost-benefit analysis to prioritize repairs that maximize operational lifecycle value, performing analyses that consider repair costs, operational impact, projected asset longevity, an asset substitution scheme, or an asset replacement scheme, and supporting comparative evaluation through a scenario comparison module configured to evaluate the predicted outcomes of different inspection scenarios.

[0014] Example benefits of the present disclosure include improving collaboration and responsiveness with real-time or near real-time status, performance metrics, and error remediation during tool deployment, by establishing real-time or near real-time communication channels between field operators and off-site engineers with shared access to extension code, testing results, or deployment status information, delivering real-time feedback, performance metrics during operation, and error notifications with suggested remediation steps, and enabling common understanding via interfaces displaying a comparison of at least selected ones of the different inspection scenarios.

[0015] Example benefits of the present disclosure include elevating model fidelity and spatial reasoning by registering ultrasonic, photogrammetric, and LIDAR data to a normalized, versioned asset model, using capabilities for overlaying inspection data onto the 3D model for visualization of asset condition, integrating laser scanning or LIDAR data to enhance geometric accuracy and spatial resolution of the model, and employing a visualization engine configured to render the asset geometry and to overlay linked datasets in deterministic spatial registration using the compass orientation, component bounds, and feature geometry.

[0016] Example benefits of the present disclosure include enabling deterministic visualization and rapid field validation through compressed rendering of large ultrasonic datasets and interactive overlays, by compressing A-scan and B-scan data into optimized file formats for fast loading and rendering, utilizing indexed storage structures for rapid signal access; hierarchical data organization; compressed waveform data; and integrated location information, and providing a user interface to manipulate visualization parameters in real-time, compare current readings against historical data, and annotate inspection findings.

[0017] Example benefits of the present disclosure include supporting robust scenario analysis and optimization for inspection and operations, including environmental and business conditions, to balance risk, performance, and cost, via a system with a simulation module, an outcome analysis module, and a scenario comparison module, that incorporates environmental condition dataPATENT Attorney Docket No. GROB-0028-WO comprising temperature fluctuations, humidity levels, atmospheric conditions, or seasonal variations, and further incorporates business condition data to contextualize tradeoffs.

[0018] Example benefits of the present disclosure include providing consistent, versioned asset histories that preserve pre- and post-repair states for accurate trending, audits, and regulatory documentation, by defining an effective date identifying applicability of a version of an asset model with an associated index object and a generation counter, enabling temporal workflows that include aligning current photogrammetric models with historical models, quantifying geometric changes over time, and documenting repair effectiveness, and capturing installation date, effective date, and maintenance and repair history references as lifecycle metadata.

[0019] Example benefits of the present disclosure include facilitating cross-asset benchmarking to identify best performers and outliers, guiding targeted interventions and best-practice propagation, through operations that benchmark asset performance against similar assets within the fleet to prioritize actions across multiple assets, compare strategies using a scenario comparison module configured to evaluate the predicted outcomes of different inspection scenarios, and analyze historical operational data to refine scenario predictions using past performance and maintenance history.

[0020] Example benefits of the present disclosure include enhancing safety by validating proposed actions against predefined standards and issuing alerts on deviations or unexpected simulation outcomes, by requiring a validation operation to compare potential outcomes of determined actions against at least one of predefined safety standards or predefined operational standards, generating stakeholder notifications when simulation results indicate unexpected outcomes or deviations from standard repair parameters, and applying automated validation algorithms configured to assess signal quality in real-time and alert operators to areas requiring additional inspection or validation.

[0021] Example benefits of the present disclosure include increasing data quality and coverage with real-time field validation of signal integrity, completeness, and anomaly detection during data collection, by analyzing the data using a real-time processing module to provide instant feedback on asset condition, applying routines that assess signal quality in real-time, identify potential measurement anomalies, and verify data collection coverage, and complementing validation with temporal analyses that track degradation patterns as additional assurance.

[0022] Example benefits of the present disclosure include unifying planning outputs — inspection plans, maintenance schedules, repair simulations, and reports — within a single asset-focused platform, by providing a user interface for the generation of planning artifacts including inspection plans, maintenance schedules, repair simulations, and reporting outputs, integrating a repair simulation module with an instruction generation module, and producing maintenance schedulePATENT Attorney Docket No. GROB-0028-WO recommendations based on predicted wear patterns, projected degradation rates, resource availability, and operational impact assessments.

[0023] Example benefits of the present disclosure include scaling across heterogeneous asset classes with an extensible data structure that maintains stable identifiers for reliable data association and analytics over time, by defining a model type selected from an enumeration comprising vertical tanks, horizontal tanks, piping, legacy boiler assets, ships, missile silos, and legacy cones, enriching context with tags identifying fleet identifier, regulatory jurisdiction, and applicable standards, and ensuring consistent registration through scene configuration and bounds data comprising a compass orientation value, component bounds, and feature geometry ranges.

[0024] The provided example benefits are illustrative and non-limiting. A given embodiment of the present disclosure may provide all or a portion of any one or more of the example benefits, and / or may provide other benefits not listed in the example benefits.BRIEF DESCRIPTION OF THE FIGURES

[0025] Fig. 1 is a block diagram of an example inspection-based asset management system showing data collection, processing, action selection, and feedback modules.

[0026] Fig. 2 is a flow diagram of an example method for asset management illustrating receiving data, asset- and fleet-level analysis, action determination, execution, evaluation, and adjustment.

[0027] Fig. 3 is a block diagram of an example system for a field support interface depicting a field-accessible interface, data access, extension management, deployment, validation, feedback, backup, and synchronization modules.

[0028] Fig. 4 is a flow diagram of an example method for creating, deploying, and updating extensions through a field support interface with real-time analysis and validation.

[0029] Fig. 5 is a block diagram of an example permission management system illustrating rolebased, action-based, and asset-specific controls combined in a multidimensional matrix with validation.

[0030] Fig. 6 is a flow diagram of an example method for managing user role permissions, including role definition, access determination, asset / tool access management, real-time verification, and dynamic updates.

[0031] Fig. 7 is a block diagram of an example repair planning system showing data input, repair planning, repair simulation, instruction generation, and feedback integration within a platform.

[0032] Fig. 8 is a flow diagram of an example method for repair planning illustrating data reception, analysis to identify repair areas, simulation of repair actions, instruction generation, and dynamic updating.PATENT Attorney Docket No. GROB-0028-WO

[0033] Fig. 9 is a flow diagram of an example simulation and optimization process showing scenario generation, machine learning, dynamic adjustment, outcome analysis, comparison, data collection, and maintenance scheduling.

[0034] Fig. 10 is a flow diagram of an example photogrammetry-based inspection process depicting image capture, 3D model creation, key feature detection, data overlay, and decision guidance.

[0035] Fig. 11 is a flow diagram of an example field data processing method showing compression and rendering of ultrasonic data, real-time analysis, organization, and interactive user interfaces.

[0036] Fig. 12 is a user interface depiction illustrating layered visualization of inspection snapshots with adjustable opacity and comparative overlays.

[0037] Fig. 13 is a block diagram of an example asset NDT platform showing user interface, visualization, validation, planning, reporting engines, and communications, together with asset libraries and databases.

[0038] Fig. 14 is a user interface depiction of an example asset with inspection overlays illustrating thickness mapping, nominal course values, and repair planning controls.

[0039] Fig. 15 is a user interface depiction of an asset model editor illustrating component and feature editing, weld management, plate definitions, and inspection layer controls.

[0040] Fig. 16 is a user interface depiction showing corrosion-related overlays, localized measurements, and repair planning options for an inspected component.

[0041] Fig. 17 is a user interface table view illustrating inspection job listings, import controls, publishing states, and component references.

[0042] Fig. 18 is a table view of example asset records showing last inspection dates, areas, components, ERP identifiers, and update status.

[0043] Fig. 19 is a user interface depiction of an asset model editor illustrating “diff from nominal” visualization, plate and feature labeling, and repair plate annotations.

[0044] Fig. 20 is a mechanical-style visualization of a shell component illustrating course nominal thickness values, plate boundaries, fixed sensor locations, and repair plate positioning.DETAIEED DESCRIPTION

[0045] Robots equipped with advanced sensors, cameras, and data processing capabilities can autonomously collect detailed information about the condition of assets. However, the data collected by these robotic systems is frequently underutilized in optimizing fleet-wide operations. Often siloed within individual asset-level analyses, the insights fail to translate into actionable strategies for broader fleet management. Without integration into centralized platforms or advanced analyticsPATENT Attorney Docket No. GROB-OQ28-WO systems, critical data points are not leveraged to identify systemic issues, prioritize maintenance across fleets, or predict failures before they escalate.

[0046] To utilize full potential of robotic monitoring and asset management, facilities should not only collect data but also connect, analyze, and act on it at the fleet level. Current systems typically handle inspection data, operational metrics, and maintenance planning as separate functions. This segmented approach creates inefficiencies, increases the risk of suboptimal decisions, and fails to capture valuable learning opportunities across similar assets. Furthermore, existing solutions lack effective ways to validate and adjust asset management decisions based on actual outcomes or to leverage insights from offset assets - similar assets operating under comparable conditions.

[0047] Prior attempts to address these challenges have focused on either individual asset monitoring or basic fleet-wide metrics, without providing the integrated analysis and feedback for optimized asset management. These solutions often fail to incorporate learning from historical performance or outcomes from previous decisions, leading to repeated inefficiencies and missed opportunities for improvement.

[0048] Without limitation to any aspect of the present disclosure, descriptions that can enhance understanding of some of the terminology used herein can be found in the following U.S. Patent Application: US publication No. US 2024-00112100 Al, filed 2 OCT 2023, and entitled SYSTEM, METHOD, AND APPARATUS TO INTEGRATE INSPECTION DATA AND BUSINESS ANALYSIS (GROB-0012-U01), which is incorporated herein by reference in the entirety for all purposes.

[0049] Systems and methods described herein address these limitations by providing an integrated system that combines multi-source data analysis, asset and fleet-level decision-making, and outcome-based optimization in a unified platform. The system enables facilities to leverage inspection data, operational metrics, and historical performance information to make more informed decisions about maintenance, repairs, replacements, and monitoring strategies. By incorporating feedback from actual outcomes and insights from offset assets, the system continuously refines its decision-making processes and provides increasingly optimized asset management recommendations .

[0050] As used herein, “fleet” relates to or represents a related group of industrial assets or facilities that share meaningful characteristics or relationships for analysis and decision-making purposes. This grouping can be defined along several dimensions, including common ownership or operational control, shared purpose or role, geographic relationship, process relationship, asset type similarity, similar operating conditions, regulatory context, or shared risk profile. For example, a fleet could encompass all storage tanks at a single facility, or alternatively, all storage tanks of a particularPATENT Attorney Docket No. GROB-0028-WO design across multiple facilities. The members of a fleet can be adjusted based on the specific analysis goals and decision-making needs of the organization.

[0051] Referencing Fig. 1, an example system 100 for inspection-based asset management is schematically depicted. The system may be configured for managing industrial assets based on inspection data, integrating multiple levels of analysis and feedback to optimize asset management decisions. The system may include data collection 102 elements that may include one or more sensors 106 or sensor networks (e.g., inspection robots) that collects operational data related to monitored assets. The sensors may include ultrasonic thickness measurements, corrosion monitoring devices, temperature sensors, pressure gauges, flow meters, or any other devices that can provide meaningful data about asset conditions and performance. Data collection may be managed by a data collection module 110. The data collection may be continuous (e.g., from permanently mounted sensors) or periodic (e.g., from inspection robots or manual measurements).

[0052] Collection may include collection at the asset level. At the asset level, individual performance metrics, degradation patterns, and operational characteristics specific to the monitored asset may be collected. Collection may include collection at the fleet level. At the fleet level, collection may include data related to the asset's performance compares to similar assets, patterns across multiple assets, and / or system-wide implications of asset conditions.

[0053] An example system may include processing elements 112 including a data processing unit 122 configured to analyze the collected data. Based on this analysis, an action module 134 may be configured to determine appropriate responses by evaluating multiple parameters. Parameters may include factors such as asset criticality, maintenance costs, operational impact of downtime, regulatory requirements, budget constraints, and available resources. The action module can recommend various responses such as immediate repairs, scheduled maintenance, operational adjustments, enhanced monitoring, or equipment replacement. In embodiments, processing may include fleet analysis 116 and / or asset analysis 114 of the data.

[0054] The system incorporates feedback mechanisms 136 to continuously improve its decisionmaking capabilities. The asset feedback module 138 tracks the outcomes of previously determined actions for specific assets and adjusts the action module's 134 parameters accordingly. For example, if a particular maintenance strategy proves more or less effective than predicted, the system adjusts its decision-making parameters to reflect this learning.

[0055] System may include a fleet feedback module 140 that incorporate historical data from related assets to further refine the action module's parameters. This might include data about how similar assets have performed under comparable conditions, the effectiveness of different maintenance strategies across multiple assets, or patterns of degradation observed in similar equipment. ThisPATENT Attorney Docket No. GROB-0028-WO fleet-level feedback allows the system to leverage insights gained from a broader base of experience and helps prevent the repetition of suboptimal decisions across multiple assets.

[0056] In embodiments, the system enables action selection with actions elements 124. Action elements 124 may be configured to evaluate multiple response options, including repair 126, replacement 128, maintenance 132, or enhanced inspection 130 of assets. Actions may reflect both immediate needs and long-term implications. For example, when determining whether to repair or replace an asset, the system considers factors such as: repair costs, remaining useful life, replacement cost, operational impact, production output over time related to the asset, a trajectory of any one or more of these, an offset value of any one or more of these, and / or the performance history of offset (e.g., similar physical assets, and / or similarly positioned in a production environment, etc.) assets that have undergone comparable interventions. In certain embodiments, multiple actions are factored together to estimate the impact, for example replacement of an asset can include determining the impact on maintenance and / or service schedules, utilization of inspection operations to mitigate risks in the plan and / or to extend, synchronize, or desynchronize maintenance and / or service schedules for a number of assets, to adjust inspection schedules over time (e.g., as an asset ages, expends useful life due to production, wear, and / or based on events, and / or as the production or utilization of the asset changes over time). In certain embodiments, inspection operations can be adjusted to support any aspect of the asset, facility, or a group of related assets or facilities, for example and without limitation: adjusting inspection operations to cluster inspections into efficient groups; adjusting inspection operations with maintenance, service, or capital management considerations to support planned downtime for assets and / or to minimize downtime for assets; and / or adjusting inspection operations to support efficient generation of a knowledge base for offset assets (e.g., performing an inspection that is commercially marginal with respect to the asset and / or facility, but which contributes to converging on best practices or other leveraged utilization of the inspection data with offset assets, sufficient to be economically positive to the group of related assets or facilities).

[0057] Efficiency, as utilized herein, should be understood broadly, and relates to an increase in a particular output relative to an amount of a particular input. For example, the input may be a cost parameter, such as a time value, dollar value, lost production value, production disruption value, or the like. In certain embodiments, a change in risk and / or a change in risk margin may be considered an input or cost for efficiency determination. Example and non-limiting outputs include benefit parameters such as: increased production; generated data (and / or generated useful data); and / or an increased service parameter (e.g., total uptime, throughput, capacity, utilization, etc.). In certain embodiments, an increase in efficiency contemplates an increase in the output parameter, a decrease in the input parameter, an increase in the output parameter that is greater than a correspondingPATENT Attorney Docket No. GROB-0028-WO increase in the input parameter, a decrease in the input parameter that is greater than a corresponding decrease in the output parameter - for example where the increase and / or decrease is understood relative to a previously known system, a previous version of the contemplated system, a nominal system, and / or a model system. It can be seen that certain parameters may be understood as either an input / cost parameter or an output / benefit parameter, and / or that both input and output parameters may be “cost” parameters. The examples are provided to illustrate aspects of the present disclosure, and are not limiting. In certain embodiments, the system is configured to notionally support an increase in efficiency for an aspect, but the input or output parameters and / or changes in efficiency are not formally determined.

[0058] In embodiments, the system 100 may integrate with external operational systems 108, including process control systems, maintenance management software, and enterprise resource planning platforms. Data from the external systems 108 may provide context for both asset-level and fleet-level analysis. For instance, production data can help correlate asset degradation with specific operating conditions, while maintenance records can reveal patterns in equipment reliability across different operating scenarios. A particular context that may be relevant can depend upon the asset, facility, and / or selected groups of these that are relevant to a user of the system, and which relevancy may vary according to the role of the user, a particular operation being performed by the user on the system, operating conditions of any asset and / or facility, or the like. In certain embodiments, the context may be determined according to default parameters, indicated user preferences, preferences entered by another user on behalf of the user (e.g., a manager, system administrator, compliance personnel, one user tagging and marking up a view for another user, etc.). In certain embodiments, the context may be determined according to operations being performed by the user, and / or indicated intent of the user (e.g., user selecting a financial, operational, and / or service view while interacting with the system). In certain embodiments, the context assists in surfacing cross relationships between inspections and other aspects of the system, such as maintenance costs, service costs, capital expenditures over time, production effects over time, revenue streams over time, or the like, so the user can more readily confirm an improved inspection schedule while understanding the consequences to the remainder of the system. Accordingly, depicted data can help the user see several aspects of the system in the same view, and more rapidly converge on an improved and / or optimized adjustment to inspection operations, maintenance operations, service operations, production schedules, capacity, and / or sequencing, capital expenditures, or the like. It can be seen that certain aspects for the improvement and / or optimization may be performed by expert systems, machine learning algorithms, and / or iterative improvement and / or optimization algorithms of anyPATENT Attorney Docket No. GROB-0028-WO type. Any such automated adjustments, as set forth throughout the present disclosure, may be provided as a suggestion to a user, and / or implemented automatically in whole or part.

[0059] An optimization algorithm, and / or an optimization routine or operation, as utilized herein, should be understood broadly. Operations that tend to improve outcomes over time should be understood to be an optimization routine, even where an actual optimal outcome is not achieved, and / or if it is not known whether an outcome is an optimal outcome. It will be understood that iterative improvements of an outcome, and / or determinations whether the outcome is still being improved, may be a part of an optimization algorithm. It will be understood that an outcome that is improved and / or optimal at a given time or operating condition may not be an improved and / or optimal output at a later time or operating condition, and an optimization algorithm may be continued even where the algorithm otherwise appears to be at an optimal condition. Such continued operations may cause the system to move away from the improved or optimal condition for brief periods, where further continued operations will move the system back to the improved or optimal condition, and will still be considered to be a part of the optimization algorithm or routine throughout such operations.

[0060] In embodiments, the system may include a predictive analytics component 120 configured to combine historical trends with real-time data to forecast potential asset failures. The system analyzes patterns of degradation, correlates operating conditions with failure modes, and identifies early warning indicators that precede significant problems. This predictive capability may include analysis of complex interactions between multiple factors that might contribute to asset failure. In certain embodiments, the predictive analytics component 120 observes failures or off-nominal conditions of the asset or offset assets, and determines signals in the available data, and / or trends in the available data, that precede and have predictive value for the failure or off-nominal condition. That available data can include any inspection data, maintenance data, service data, production data, operating cost data, material cost data, installation and / or integration cost data, or the like. In certain embodiments, the available data includes policy or compliance data, for example minimum requirements for inspections, asset physical characteristics, downtime for inspection operations, or the like, that may be dictated by and / or responsive to requirements set by regulation, policies of a related entity, industry best practices, or the like. In certain embodiments, the available data includes determining trends, for example a wear model, from offset asset data, production data, operating condition tracking data for the asset (and / or offset assets), or the like.

[0061] In embodiments, the system may be configured to dynamically adjust data collection strategies based on the evaluated outcomes of previous decisions and actions. The system analyzes the predictive value and utility of different types of sensor data in relation to actual assetPATENT Attorney Docket No. GROB-0028-WO performance and maintenance outcomes. This analysis includes evaluating which sensor measurements provided early warning of problems, which data streams best correlated with actual asset conditions, and which combinations of measurements led to the most accurate predictions of asset behavior.

[0062] When specific types of sensor data demonstrate high value in predicting failures or optimizing maintenance decisions, the system can automatically adjust collection parameters for those data streams. These adjustments may include increasing data collection frequency, expanding the number of collection points, deploying additional sensors of that type, or increasing the resolution or sensitivity of measurements. For example, if ultrasonic thickness measurements from certain locations prove particularly indicative of developing problems, the system may increase the frequency of measurements at those locations or add measurement points in similar areas.

[0063] Adjusting data collection parameters in a robotic system manifests as dynamic, real-time changes to the robot’s operational behaviors. In one example, adjustments may include sensor configuration adjustment (e.g., increase the frequency of ultrasonic measurements on a thinning pressure vessel wall or switch to higher-resolution imaging when it detects surface cracks), path optimization (e.g., adjusting or prioritizing inspection routes to focus one areas of higher risk or importance), mode selection (e.g., robots equipped with multiple inspection modes, such as visual, ultrasonic, or infrared, can switch between them based on detected conditions), or environmental adaptation (e.g., lighting for cameras or vibration thresholds for sensors can be adapted to match changing environmental conditions, such as reduced visibility or elevated noise levels).

[0064] Embodiments of the system can identify sensor data streams that provide limited value in predicting failures or optimizing decisions. This assessment considers factors such as the correlation between measurements and actual outcomes, the cost of data collection, and the redundancy of information provided by different sensors. When data streams are determined to provide limited value, the system can reduce collection frequency, decrease the number of collection points, or reallocate sensors to more informative locations.

[0065] Resources freed up by reducing low-value data collection can be redirected to more informative measurements. This reallocation may involve physical redeployment of sensors, adjustment of data collection schedules, or reallocation of data processing resources.

[0066] In embodiments, the system may utilize historical insights and learned patterns from existing assets to accelerate the optimization of management strategies for newly added assets. This transfer learning approach leverages the system's accumulated knowledge about asset behavior, degradation patterns, and effective management strategies across similar types of equipment, enabling rapid deployment of optimized monitoring and maintenance approaches for new assets.PATENT Attorney Docket No. GROB-OQ28-WO

[0067] Embodiments of the system may be configured to identify relevant historical assets for comparison based on multiple criteria, including but not limited to physical characteristics (size, design, materials), operating conditions (temperature, pressure, contents), environmental factors (location, climate, exposure), maintenance history, and historical performance patterns. By analyzing the similarities and differences between new and existing assets, the system can identify which historical insights are most likely to be applicable to the new asset.

[0068] When a new asset is added to the system, historical data from similar assets may be used to initialize various operational parameters and decision thresholds. These parameters may include expected degradation rates, optimal inspection intervals, maintenance trigger points, and alert thresholds for various monitored conditions. This initial parameter setting may be based on demonstrated patterns of effectiveness across the fleet of similar assets, rather than starting with generic default values or waiting to develop asset-specific historical data.

[0069] While applying historical insights from similar assets, the system may maintain sensitivity to unique characteristics or operating conditions of the new asset. The transferred learning provides a starting point that is continuously refined based on actual performance data from the new asset. The system may monitor the accuracy of transferred predictions and recommendations, adjusting the weight given to historical insights based on demonstrated applicability to the new asset.

[0070] In embodiments, the system implements comprehensive real-time condition monitoring at both individual asset and fleet-wide levels. The monitoring may incorporate multiple data streams including sensor measurements, operational parameters, performance metrics, and environmental conditions. The system may continuously analyze these inputs to assess current asset condition, identify developing trends, and evaluate performance relative to expected parameters.

[0071] Fleet-wide performance monitoring aggregates data across multiple assets to establish baseline performance metrics, identify common patterns, and detect anomalies that may not be apparent when assets are monitored in isolation. This broader perspective enables the system to differentiate between normal variations and significant deviations that warrant attention.

[0072] The system may use this real-time monitoring data to dynamically adjust maintenance schedules. Adjustments may consider multiple factors including current asset condition as indicated by sensor measurements, rate of change in monitored parameters, historical degradation patterns, operational context and demands, resource availability, maintenance opportunity windows, impact on related assets or processes, fleet-wide performance trends, seasonal or cyclical factors, and / or the like.

[0073] When determining maintenance timing, the system evaluates the trade-offs between early intervention and extended operation. This analysis considers the cost of maintenance activities, thePATENT Attorney Docket No. GROB-0028-WO risk of failure, the impact of downtime, and the expected benefit of maintenance actions. The system may accelerate maintenance when monitoring indicates faster-than-expected degradation or defer maintenance when assets are performing well beyond standard intervals.

[0074] A dynamic can be utilized to reduce unnecessary maintenance activities by ensuring that maintenance is performed only when warranted by actual conditions or reliable predictive indicators. The system can identify assets that consistently perform beyond standard maintenance intervals, allowing for the extension of maintenance schedules where appropriate while maintaining adequate safety margins.

[0075] An example system may include asset performance benchmarking 118 for prioritizing fleetwide actions. By comparing performance indicators across similar assets, the system identifies both best practices and potential areas for improvement. In certain embodiments, the asset performance benchmarking 118 can be based on any input parameters or output parameters related to performance and / or efficiency (e.g., reference the description of “efficiency’-preceding), and can be utilized to determine outliers, track performance against a target, set a target value that is realistic and achievable, to monitor performance of the asset and / or fleet, and / or as a tracking parameter for iterative improvement and / or optimization operations.

[0076] An example system may include anomaly detection to identify deviations that might indicate developing problems. Early-stage anomalies may be evaluated in the context of historical failure patterns and fleet-wide experience to assess their significance and potential progression. This early warning capability enables proactive intervention before minor issues develop into major problems.

[0077] In an example system, data collection frequency may be dynamically adjusted based on asset criticality and / or detected anomalies. Critical assets, or those showing signs of potential problems, may be configured to receive more frequent monitoring, while stable, less critical assets might require less intensive data collection.

[0078] In one example, each asset may be assigned a criticality score based on factors such as its function, operating conditions, maintenance history, and potential failure impact. This criticality assessment is stored as metadata linked to the asset's digital model within the system's hierarchical asset database. During routine inspections, the robotic devices collect sensor data such as ultrasonic thickness measurements, visual imagery, and surface profiles. This inspection data is processed to detect any anomalies or signs of degradation by comparing the current asset condition to its nominal baseline specifications and past inspection results. Assets flagged as high criticality or exhibiting concerning anomalies are automatically scheduled for more frequent inspections by the robotic fleet. The system may be configured to optimize the deployment of the robotic inspectors to prioritizePATENT Attorney Docket No. GROB-OQ28-WO coverage of these high-risk assets while scaling back the inspection frequency for stable, less critical assets.

[0079] Embodiments may include an interface 104 configured for operator input. The interface may allow operators to supplement automated data collection with manual inputs regarding operational conditions, observed anomalies, or maintenance activities. This human input may include context that might not be captured by automated systems alone and is incorporated into both asset-level and fleet-level analysis.

[0080] In accordance with various embodiments of the present disclosure, methods are provided for managing assets in inspection-based systems. Fig. 2 depicts a flow diagram of one example method 200. The method may comprise receiving sensor and operational data from monitored assets 202. The sensor data may include, but is not limited to, ultrasonic thickness measurements, corrosion monitoring data from fixed sensors, and photogrammetric data. The operational data may comprise process parameters including fill levels, specific gravity values, temperature readings, and pressure measurements.

[0081] In certain embodiments, the method implements asset-level analysis 204 to determine asset conditions. The analysis may evaluate thickness measurements against industry standards. The analysis may calculate remaining life projections based on current measurements and historical trends. In some embodiments, the analysis generates condition assessments incorporating multiple data layers to provide comprehensive asset evaluation.

[0082] The method may further implement fleet-level analysis 206 across multiple assets to identify patterns and trends. This analysis may reveal correlations between operational conditions and degradation rates. In some embodiments, the fleet-level analysis enables comparison of similar assets under varying operating conditions to optimize maintenance strategies across an asset portfolio.

[0083] Based on the asset-level and fleet-level analyses 206, the method may determine appropriate actions 208. The actions may include, but are not limited to, immediate repairs, operational adjustments, or modified inspection schedules. In one embodiment, where analysis indicates accelerated corrosion, the method may recommend reducing fill levels or adjusting product specifications. An example method includes executing the determined action(s) 210.

[0084] The method may further comprise evaluating action outcomes 212 through updated sensor data. This evaluation enables verification of action effectiveness and refinement of future decisions. In certain embodiments, the evaluation assesses whether implemented actions achieved desired outcomes, such as reduced corrosion rates or extended asset life.PATENT Attorney Docket No. GROB-OQ28-WO

[0085] In accordance with various embodiments, the method implements dynamic adjustment of asset management strategies 214 based on observed outcomes. These adjustments may modify inspection intervals, maintenance timing, or operational parameters. The method may incorporate learning from both successful and unsuccessful outcomes to optimize future asset management decisions while maintaining compliance with operational requirements and industry standards.

[0086] In accordance with embodiments of the present disclosure, a system for providing a field support interface in an inspection-based asset management process may enable field personnel to rapidly develop and deploy extensions that add new functionality while maintaining integration with the core platform. The system includes interfaces and modules that manage extension development, data access, and deployment while enforcing appropriate security controls and maintaining system stability. Extensions, as used herein, may reference any type of operational capability available to the field user, for example to provide operational capability for validating inspection data, annotating inspection data and / or displays of inspection outcomes, performing repetitive tasks (e.g., formatting outputs, updating multiple steps of an inspection operation, performing sequential validation operations on multiple sensors and / or payloads of an inspection robot, evaluating the asset and collected data to ensure inspection operations are fully completed, ensuring that all relevant inspection data is uploaded, etc.), and / or performing sensitive tasks (e.g., validating and / or applying calibrations, ensuring that operational checklists are completed, and / or modifying or creating checklists). The utilization of extensions allows for supporting operations to be applied in the field -for example tools to be utilized at the inspection site can be created and deployed by supporting personnel such as an analyst, dispatcher, engineering manager, or the like. The utilization of extensions allows for developing expertise and / or best practices in related assets, and / or over time for a particular facility, type of asset, or the like, to be developed consistently and be conveniently available for inspection operations on related assets; for example a group of extensions may be created and / or iteratively improved over time, and deployed as a package of extensions for utilization in relevant inspection operations to ensure that those best practices are readily available. The utilization of extensions further allows for confirmation that the appropriate operations are being utilized, for example by tracking utilization of the extensions during inspection operations. The utilization of extensions further allows for field operators to provide input into inspection operations and / or analysis, for example by allowing field operators to create and / or modify extensions, and provides a convenient and reliable interface for those created and / or modified extensions to be captured in the system, confirmed by analysts and / or other experts, and to be utilized by other operators (e.g., by exposing the new and / or modified extensions to other operators, and / or byPATENT Attorney Docket No. GROB-0028-WO including the new and / or modified extensions into a relevant package of extensions, for example to be utilized by inspection operators on related assets, for a particular facility, etc.).

[0087] Referencing Fig. 3, an example system 300 for providing a field support interface in an inspection-based asset management is schematically depicted. An example system may include a field-accessible interface 302 for accessing and modifying system extensions. The field-accessible interface may provide forward deployed engineers with capabilities to rapidly add new tools and functionalities to respond to customer-specific requirements. The interface may enable interaction with different data layers including ultrasonic data, photogrammetric data, and other sensor data collected during inspections.

[0088] An example system may include an extension management module 308 configured to allow the creation, customization, or deployment of extensions. The extension management module may operate through a repository where different teams can contribute code. The module may implement a permissions model 332 to grant access to extensions based on user roles and asset types. The module may enable forward deployed engineers to merge code quickly to test new functionality while maintaining integration with core product code.

[0089] An example system may include a data access module 306 for retrieving operational and inspection data in real time. The data access module may access inspection data including ultrasonic readings, thickness measurements, corrosion data, and photogrammetric models. The module may retrieve operational parameters including fill levels, specific gravity values, temperature data, pressure data, and chemical composition data. The module may enable rapid lookup of individual data points, such as specific ultrasonic scans within a larger dataset.

[0090] An example system may include a deployment module 304 for deploying extensions in response to selection from the field-accessible interface. The deployment module may be configured to identify data for the deployed extension. The deployment module may manage extension dependencies and data requirements. The module may coordinate with role-based access controls to ensure appropriate data access for each extension. The module may enable rapid iteration and testing of new extensions in the field while maintaining system stability and security.

[0091] The system may enable forward deployed engineers to rapidly develop and deploy new functionality in response to customer needs while maintaining integration with the core platform architecture and security model. Extensions may be developed, tested, and deployed without requiring changes to the underlying platform via customizations 330. For example, a user may automate a workflow on the system (e.g., a series of operations, accessing tools and / or menus, or the like) and provide that as an extension to reduce errors and repetitive actions. In another example, a user may prepare a view of a particular asset, facility, or selected group(s) of these, prepare overlayPATENT Attorney Docket No. GROB-OQ28-WO data in a desired manner, and save the view and / or operations to create the view as an extension. The utilization of extensions allows users to share capability with other users, to utilize an extension as a template for creating additional views or workflows, and to ensure consistency within a selected group of users (e.g., an inspection team) for analysis, monitoring, and / or execution of operations related to the asset(s) and / or facility(ies).

[0092] In certain embodiments, the system enables customization of extensions without requiring centralized information technology support. The field-accessible interface may provide tools allowing forward deployed engineers to modify extension parameters and create new analytical workflows. The system may store extension configurations in a repository, wherein multiple engineering teams may contribute modifications / customizations. In some embodiments, the interface enables rapid testing and deployment of modified extensions while maintaining integration with core platform functionality.

[0093] The deployment module 304 may comprise validation tools 316 for analyzing extension performance in real-time. In one embodiment, these tools monitor extension response times when processing inspection data such as ultrasonic readings or photogrammetric models. The validation tools may measure data processing accuracy by comparing results against reference values. The system may track resource utilization metrics including processor usage, memory allocation, and data throughput rates during extension operation.

[0094] Embodiments may include a collaboration module 328 establishing secure communications between field operators and off-site engineering teams. The module may provide shared access to extension code repositories, testing environments, and deployment status information. In some embodiments, the collaboration 328 may be configured to enable real-time coordination via a sync module 322 during extension modification and deployment activities.

[0095] In embodiments, the extension management module may implement role-based access controls governing extension development and deployment capabilities. In certain embodiments, these controls restrict access based on user roles including, but not limited to, plant managers, corporate managers, and system administrators. The access control system may limit deployment capabilities to specific asset types or facilities. The system may maintain audit records comprising user identification data, timestamp information, and modification details for all extension changes.

[0096] In some embodiments, the field-accessible interface supports offline extension functionality via offline data 324. The system may maintain local copies of extension code and required operational data. During periods without network connectivity, the system may enable continued extension operation using locally stored resources. Upon restoration of network connectivity, the system may automatically synchronize local modifications with centralized platforms and maintainPATENT Attorney Docket No. GROB-0028-WO real-time data 326. The synchronization process may resolve any conflicts between local and centralized data versions.

[0097] Embodiments may include a backup module 320 configured to store extension modifications and operational data in cloud storage systems. The backup module may maintain comprehensive modification histories including user activities, deployment records, and system configurations. In certain embodiments, the backup system enables restoration of previous extension versions or system states. The system may support audit requirements by maintaining detailed records of all system modifications and operational activities.

[0098] In embodiments, the deployment module 304 may provide status feedback 318 to field operators regarding extension deployment and operation. In some embodiments, this feedback includes extension initialization status, data access verification, and operational performance metrics. The system may generate error notifications comprising error descriptions and suggested remediation steps. The feedback system may maintain logs of deployment activities including success indicators, error conditions, and resolution details.

[0099] In accordance with various embodiments of the present disclosure, methods are provided for implementing field support in inspection-based asset management systems. Fig. 4 depicts a flow diagram of one example method 400. The method may include the step of accessing a field support interface 402 configured to retrieve real-time operational and inspection data.

[0100] The method may further include the step of creating and modifying system extensions through the field interface 404. In certain embodiments, forward deployed engineers may implement rapid modifications to extension parameters or create new analysis workflows. The system may enable development of customer-specific features while maintaining integration with existing data layers and functionality. The extension development process may support immediate response to field requirements while preserving system stability.

[0101] In some embodiments, the method implements deployment procedures for extensions created or modified through the field interface 406. The deployment process may comprise automated validation steps to verify extension compatibility and performance characteristics. The system may evaluate data access requirements, monitor resource utilization levels, and confirm proper integration with existing functionality prior to deployment authorization.

[0102] The method may further include real-time analysis of operational and inspection data through the field interface 408. In one embodiment, when processing inspection data such as ultrasonic tank measurements, engineers may verify data quality and perform initial analysis at the inspection site. The system may support immediate validation of signal quality, measurement accuracy, and data coverage to ensure inspection completeness.PATENT Attorney Docket No. GROB-OQ28-WO

[0103] In accordance with various embodiments, the method may include dynamic extension updates based on real-time requirements 410. The system may maintain version control records, document modifications, and monitor performance metrics following updates. The method may incorporate user feedback to guide extension refinements while maintaining system security and stability.

[0104] In accordance with embodiments of the present disclosure, a system for managing permissions in an inspection-based asset management and field support interface implements a multilayered permission architecture that combines role-based, action-based, and asset-specific access controls through a multidimensional permission matrix. This approach enables organizations to maintain robust security while supporting the deployment of new capabilities required in modern industrial operations.

[0105] The system may provide particular advantages when managing permissions across organizational hierarchies, from plant-level operators to corporate managers, while enabling field personnel to rapidly develop and deploy new capabilities. By combining multiple permission dimensions with real-time validation, the system maintains security while preserving operational flexibility.

[0106] Referencing Fig. 5, an example system for a system for managing permissions in an inspection-based asset management platform is schematically depicted. The system may implement multiple permission control layers for managing access to data, functionality, and assets.

[0107] An example system 500 may include a role-based 504 permissions management module that may be configured to assign and manage user permissions based on organizational roles. The module may define permission sets corresponding to different organizational positions. These positions may include, but are not limited to, plant managers, corporate financial officers, site engineers, and system administrators. The module may restrict plant manager access to facilityspecific data while enabling corporate manager access to cross-site metadata. In some embodiments, the module may allow administrator configuration of granular functionality permissions within each defined role.

[0108] An example system may include an action-based permissions module 512 configured to control access to specific system operations. These operations may include extension creation, data access, analysis execution, and functionality deployment. In one embodiment, the module may implement graduated permission levels for extension development, allowing rapid testing while maintaining security controls. The action-based permissions may require multiple authorization levels for designated sensitive operations.PATENT Attorney Docket No. GROB-OQ28-WO

[0109] An example system may include an asset-specific permissions module 510 that manages access based on asset classification and location data. The module may implement hierarchical permission inheritance, wherein organizational level permissions automatically extend to associated sites and assets. The module may restrict access based on asset categories including, but not limited to, storage tanks, boilers, and flight decks. In some embodiments, the system may enable detailed control over user access to specific asset data and operational capabilities.

[0110] An example system may include a multidimensional permissions matrix 508 that evaluates role, action, and asset parameters to determine access rights. The matrix may process multiple permission requirements simultaneously before enabling system access. In certain embodiments, the matrix may verify role permissions, action authorizations, and asset access rights for each attempted operation. The matrix may support complex permission scenarios while maintaining consistent access control implementation.

[0111] An example system may include a permissions validation module 506 configured to verify user authorizations against the multidimensional matrix for each system operation. In some embodiments, the validation module may confirm role assignments, action permissions, and asset access rights before allowing operations to proceed. The system may generate validation records comprising user identification data, timestamp information, and authorization results. The validation module may issue notifications upon detection of unauthorized access attempts.

[0112] Embodiments may include interfaces enabling permission configuration across multiple control dimensions. These interfaces may support role definition creation, action permission assignment, and asset access control configuration. The interfaces may enable development of custom permission rules and audit of system access patterns. In certain embodiments, the interfaces may provide real-time permission modification capabilities while maintaining system security.

[0113] In embodiments, the permission management system 500 may integrate with extension development and deployment capabilities. The system may control extension creation, modification, and deployment permissions while maintaining security protocols. In some embodiments, the system may enable rapid extension development by authorized personnel while ensuring operations remain within defined permission boundaries.

[0114] In some embodiments, the system includes a learning engine configured to analyze historical permission patterns across multiple assets and users. The learning engine may identify common permission requirements for similar asset types or roles. Based on this analysis, the engine may recommend permission assignments when new assets or users are added to the system. For example, if engineers typically receive specific permissions for storage tank analysis, the system may automatically suggest similar permissions when new storage tank assets are added. The learningPATENT Attorney Docket No. GROB-OQ28-WO engine may continuously refine its recommendations based on administrator acceptance or rejection of suggested permissions.

[0115] Examples of the system may include granular control over specific system actions. These controls may govern data modification capabilities, allowing certain users to edit asset parameters while restricting others to view-only access. The system may regulate extension creation and deployment rights, enabling forward deployed engineers to develop new capabilities while maintaining security controls. Analysis tool access may be controlled based on user role and expertise level. The system may also manage report generation permissions, controlling what information users can extract from the system.

[0116] Administrators may create and modify custom roles to match specific organizational requirements. These custom roles may combine unique sets of system access rights, data visibility levels, and tool permissions. For example, a custom role might be created for maintenance planners with specific permissions for repair planning tools but limited access to financial data. The system may enable administrators to clone and modify existing roles to create new ones, maintaining consistent permission patterns across similar roles. Asset-specific permissions modules may implement location-based access controls. These controls may receive data describing facility location, regional boundaries, and national jurisdictions when determining access rights. For example, users may be restricted to accessing data from facilities within their assigned geographic region. The system may enforce data residency requirements by controlling data access based on user and data location. Geographic controls may integrate with organizational structures to align access rights with regional management responsibilities.

[0117] In one example of the system, the multidimensional permissions matrix may automatically update in response to external factors. Changes in asset conditions, such as critical inspection findings, may trigger automatic permission adjustments to ensure appropriate oversight. Operational requirement changes may lead to dynamic updates in access rights. The system may modify permissions in response to regulatory requirement updates or organizational policy changes.

[0118] An example system may include an administrative interface configured to provide real-time visualization and modification capabilities for the permissions matrix. The interface may display graphical representations of permission relationships across roles, actions, and assets.Administrators may directly manipulate matrix elements while the system identifies potential permission conflicts. The interface may maintain detailed logs of all permission modifications, supporting audit requirements and security reviews.PATENT Attorney Docket No. GROB-OQ28-WO

[0119] In accordance with various embodiments of the present disclosure, methods are provided for managing permissions in inspection-based asset management systems. Fig. 6 depicts a flow diagram of one example method 600.

[0120] In certain embodiments, the method includes the step of defining user roles and assigning corresponding permission sets 602. The roles may include, but are not limited to, plant managers, corporate officers, site engineers, and system administrators. Each role may receive specific permissions aligned with operational responsibilities and security requirements. The method may enable creation of custom roles to address specific organizational needs while maintaining consistent security protocols.

[0121] The method may include determining access control based on user roles 604. In some embodiments, the system evaluates role-based permissions to determine access to specific functionality including, but not limited to, data modification capabilities, extension deployment rights, and analysis tool access. The access control system may consider multiple permission dimensions when evaluating access requests.

[0122] In accordance with various embodiments, the method may include managing access to asset data and operational tools through multiple parameters 606. The system may consider asset type classifications, geographic locations, and user role assignments when determining access rights. For example, plant managers may receive access to specific facility data while corporate managers access metadata across multiple sites.

[0123] The method may include real-time permission verification for requested actions 608. The system may evaluate current user permissions against a multidimensional permission matrix before authorizing operations. This verification process may consider current role assignments, assetspecific permissions, action-based restrictions, geographic access limitations, temporary permission grants, and the like.

[0124] In certain embodiments, the method may include dynamic modification of permission sets based on changing conditions 610. The system may adjust permissions in response to one or more of role assignment changes, new system functionality, deployment, modified asset classifications, updated operational requirements, revised security protocols, and the like.

[0125] In accordance with various embodiments, a system for providing a repair tool in an inspection-based asset management process addresses complex repair planning and implementation requirements in industrial settings. Traditional repair planning often relies on disconnected data sources and manual analysis, leading to suboptimal repair strategies and resource allocation. The present disclosure describes a system that combines automated data analysis, repair simulation capabilities, and real-time feedback to optimize repair planning and execution.PATENT Attorney Docket No. GROB-0028-WO

[0126] Referencing Fig. 7, an example system 700 for implementing repair planning capabilities in inspection-based asset management platform 712 is schematically depicted. The system integrates inspection data, operational parameters, and industry standards to generate and optimize repair plans while maintaining asset integrity.

[0127] An example system may include a data input module 710. The data input module 710 may receive multiple types of asset condition data including, but not limited to, ultrasonic thickness measurements, corrosion data, and photogrammetric models. The module may ingest operational parameters including fill heights, specific gravity values, temperature data, and pressure readings. In certain embodiments, the module may access historical inspection data and maintenance records to establish degradation trends. The system may incorporate process data from control systems to understand operational impacts on asset condition.

[0128] An example system may include a repair planning module 708. The repair planning module 708 may analyze received data to identify areas requiring repair based on industry standards and operational requirements. The module may implement specific repair standards, to evaluate stresses and determine minimum thickness requirements. The system may consider factors including product specific gravity, fill height, and operational parameters when determining repair requirements. The module may automatically identify repair areas while accounting for constraints such as proximity to weld lines or minimum patch size requirements.

[0129] An example system may include a repair simulation module 706. In some embodiments, the repair simulation module enables modeling of different repair scenarios. The module may simulate outcomes of various repair approaches including partial repairs, complete replacements, or operational modifications. The simulation may predict remaining asset life under different repair scenarios by incorporating corrosion rates and operational parameters. The module may evaluate cost implications and operational impacts of different repair strategies.

[0130] An example system may include an instruction generation module 704. The system may generate detailed repair instructions based on selected repair strategies. These instractions may include specific repair locations identified on asset models, required patch sizes and specifications, welding or attachment requirements, material specifications, quality control requirements, safety considerations, and the like.

[0131] An example system may include feedback integration 702. The system may implement a feedback loop that adjusts repair plans based on real-time data collected during and after repairs. The feedback system may: monitor repair effectiveness through continued inspection data, update degradation models based on post-repair performance, adjust future repair recommendations basedPATENT Attorney Docket No. GROB-OQ28-WO on observed outcomes, validate repair quality through inspection data, document repair history for future reference, and the like

[0132] In various embodiments, the system supports multiple repair strategies based on asset condition, operational requirements, and industry standards. For example, in the case of an aboveground storage tank containing copper concentrate slurry, the system may evaluate localized patch repairs, complete shell course replacements, or operational modifications depending on degradation patterns and severity. When analyzing a storage tank with a remaining wall thickness of 0.15 inches where minimum required thickness is 0.2 inches, the system may recommend a patch repair utilizing a 12-inch by 12-inch plate of matching material grade, extending beyond the degraded area with specific welding requirements to maintain structural integrity.

[0133] For more extensive degradation patterns, the system may recommend complete shell course replacement when multiple degraded areas exist in close proximity or when remaining life calculations indicate broad-scale deterioration. The system considers factors such as proximity to existing weld lines, minimum patch size requirements, and the cumulative effect of multiple repairs when determining whether localized repairs remain viable or full section replacement becomes necessary. In cases where patch repairs would cover the majority of a shell course, the system may determine that complete replacement provides better long-term integrity and cost efficiency.

[0134] The system may alternatively recommend operational modifications rather than immediate physical repairs based on simulation results and cost-benefit analysis. These modifications might include reducing maximum fill height to decrease wall stresses, adjusting product composition to reduce corrosion rates, or implementing enhanced monitoring protocols. The system evaluates these options by simulating their impact on asset remaining life and operational efficiency, while maintaining compliance with standards.

[0135] In certain embodiments, the instruction generation module produces comprehensive repair documentation tailored to specific asset types and conditions. These instructions may include required material specifications, such as plate grade and thickness for tank repairs, along with specific welding procedures and parameters. The system incorporates relevant safety protocols, including confined space requirements, and environmental considerations.

[0136] An example system may include prioritization algorithms that evaluate multiple factors when ranking repair requirements. These algorithms may be configured to include operational data to determine criticality. Operational data may include information such as the role of an asset in production processes, potential safety implications of failure, or current utilization rates. The repair simulation module may be configured to calculate expected post-repair asset lifespan by analyzing factors including projected corrosion rates, operational stresses, or material properties. For example,PATENT Attorney Docket No. GROB-0028-WO when simulating storage tank repairs, the system may incorporate fill levels, product specific gravity, and historical degradation patterns to project remaining life under different repair scenarios.

[0137] Embodiments may incorporate industry-specific standards and regulatory requirements directly into repair planning and execution. The feedback loop enables dynamic updating of repair plans based on real-time inspection data. For instance, if ultrasonic testing during repair preparation reveals additional degradation, the system automatically adjusts repair specifications while maintaining compliance with applicable standards. The system may leverage historical repair data to improve repair outcome predictions. By analyzing repair effectiveness across similar assets and conditions, the system refines lifespan projections and repair specifications. The repair planning module implements cost-benefit analysis considering direct repair costs, operational impact of repair activities, and projected asset longevity improvements.

[0138] In certain embodiments, the system includes comprehensive post-repair evaluation. These evaluations assess repair effectiveness through inspection data analysis, comparing actual results against projected outcomes. The monitoring module tracks ongoing asset performance through continued inspection and operational data collection. When performance deviates from expected parameters, such as accelerated wall loss following a repair, the system generates alerts to relevant stakeholders.

[0139] In accordance with various embodiments of the present disclosure, methods are provided for managing and planning repairs in inspection-based asset management systems. Fig. 8 depicts a flow diagram of one example method 800.

[0140] The method 800 may include receiving inspection data from monitored assets 802. The method may analyze the received data to identify areas requiring repair intervention. In some embodiments, the method may include simulating to predict repair outcomes 804. The simulation may model different repair scenarios including, but not limited to, patch repairs, complete section replacements, and operational modifications. The system may calculate expected post-repair asset lifespans by analyzing factors such as projected corrosion rates, operational stresses, and material properties. The simulation results may inform repair strategy selection while considering operational impacts.

[0141] The method may generate detailed repair instructions based on selected repair strategies 806. In certain embodiments, these instructions may specify material requirements, welding procedures, safety protocols, and quality control measures. The instructions may incorporate industry-specific standards and regulatory requirements appropriate for the asset type and repair location. The system may generate documentation suitable for repair execution while maintaining compliance with applicable standards 808.PATENT Attorney Docket No. GROB-0028-WO

[0142] In accordance with various embodiments, the method may include real-time modification of repair plans based on feedback from the repair site 810. The system may adjust repair specifications in response to discovered conditions or changing asset parameters during repair implementation. The method may implement continuous monitoring to verify repair effectiveness and adjust future maintenance planning based on observed outcomes.

[0143] In accordance with various embodiments, a system for simulating and managing operating scenarios provides capabilities to model and evaluate different operational strategies. The system enables simulation of different to understand impacts on asset integrity and performance.

[0144] Referencing Fig. 9, an example system 900 for implementing simulations is schematically depicted. An example system may include a data collection module 924 configured to gather comprehensive operational and performance data from industrial assets and / or fleets of assets. In certain embodiments, the module integrates process control data including temperature values, pressure readings, flow rates, and chemical composition parameters from sensors 926. The system may also collect historical maintenance records 928, inspection findings, and repair documentation to establish baseline performance patterns.

[0145] An example system may include a simulation module 902. The simulation module 902 may create multiple operating scenarios 904 by adjusting operational parameters 908. For example, in a storage tank scenario, the module may simulate different fill height levels, varying from current 27 -foot levels to reduced 24-foot levels, while calculating impacts on wall stresses and corrosion patterns. The module may adjust maintenance scheduling, modeling outcomes of different inspection intervals or preventive maintenance timing. In some embodiments, the module simulates varying operational loads.

[0146] An example system may include an outcome analysis module 910. The outcome analysis module may be configured to implement predictive capabilities to evaluate scenario impacts. The module may calculate remaining life projections under different operational conditions using industry standards. In certain embodiments, the module analyzes performance metrics including throughput rates, efficiency levels, and reliability indicators. The system may evaluate failure risks by considering factors such as corrosion rates, mechanical stresses, and environmental conditions. The analysis incorporates both historical performance data 912 and industry standard requirements to generate accurate predictions 914, and to adjust maintenance scheduling 916 in response to the predicted outcomes.

[0147] An example system may include a scenario comparison module 918. The scenario comparison module enables detailed evaluation of different operational strategies. The module may compare cost implications of various scenarios 920, including maintenance costs, operationalPATENT Attorney Docket No. GROB-OQ28-WO efficiency impacts, and potential failure risks. The system may evaluate scenarios across multiple assets to optimize facility-wide operations. The comparison capabilities may include direct comparison of performance 922 parameters, such as remaining life calculations, cost-benefit analysis of different strategies, risk assessment across scenarios, resource requirement comparisons, and / or operational efficiency evaluations.

[0148] An example system may include an output module. The output module presents scenario comparison results through configurable interfaces. The module may generate visual representations of different scenarios, including 3D models showing stress patterns or corrosion projections. In certain embodiments, the module produces detailed reports comparing scenario outcomes across multiple metrics. The system may provide real-time updates as scenarios are modified, enabling interactive exploration of different operational strategies.

[0149] An example system may include integration with other platform components including asset management systems, maintenance planning tools, inspection data management, financial analysis systems, regulatory compliance modules, and the like.

[0150] An example system may incorporate environmental conditions into operational scenario modeling to improve prediction accuracy. For example, when simulating storage tank operations, the system considers ambient temperature fluctuations that may affect product viscosity or thermal expansion, humidity levels that might impact corrosion rates, and seasonal variations that could influence operational parameters. Historical operational data provides context for scenario predictions. By analyzing past performance under similar conditions, the system refines its projections of asset behavior and degradation patterns.

[0151] An example system may include machine learning 906 and real-time adaptation. In certain embodiments, the system implements machine learning algorithms that continuously improve scenario predictions. These algorithms analyze real-time operational data, comparing predicted outcomes against actual performance metrics to identify prediction inaccuracies. For instance, if predicted corrosion rates deviate from observed degradation, the system adjusts its models accordingly. The machine learning capabilities enable increasingly accurate predictions as more operational data becomes available, adapting to specific asset characteristics and operating environments.

[0152] An example system may include data collection and analysis. The data collection module gathers operational information to support scenario modeling. Operational information may include continuous sensor readings such as temperature, pressure, and thickness measurements, along with detailed equipment performance logs tracking operational parameters. Maintenance records provide context about past interventions and their effectiveness, while inspection findings offer insights intoPATENT Attorney Docket No. GROB-0028-WO degradation patterns. The system integrates this data to create comprehensive operational profiles supporting scenario development.

[0153] In embodiments, the system may prioritize scenarios that optimize asset lifespan while maintaining operational requirements. When unexpected changes occur, such as equipment malfunctions or process disruptions, the simulation module dynamically adjusts scenarios to reflect new conditions. For example, if a storage tank experiences unexpected corrosion acceleration, the system may generate new scenarios incorporating modified inspection intervals or operational parameters. This dynamic adaptation ensures continued relevance of operational recommendations despite changing conditions.

[0154] In embodiments, the system may be configured for maintenance schedule optimization. Based on scenario analysis results, the system generates optimized maintenance schedule recommendations. These recommendations consider predicted wear patterns under different operational conditions, projected degradation rates based on actual performance data, and resource availability constraints. The system may adjust maintenance timing to address anticipated issues before they impact operations while minimizing unnecessary interventions. This approach enables proactive maintenance planning aligned with actual asset conditions and operational requirements.

[0155] Embodiments presented in the present disclosure provides methods for integrating photogrammetric modeling with inspection-based asset management. Referencing Fig. 10, an example flow diagram of a method 1000 for photogrammetry inspection-based asset management is schematically depicted.

[0156] Example method may begin with systematic image capture using multiple specialized imaging devices 1002. These may include cameras mounted on robotic inspection platforms, such as the pressure vessel and tank, which simultaneously collect ultrasonic measurements and visual data. The method may also employ drone-mounted cameras for accessing elevated or difficult-to-reach areas, and handheld high-resolution cameras for detailed documentation of specific features. The image capture process may follow protocols regarding overlap, lighting conditions, and reference markers to ensure optimal photogrammetric reconstruction.

[0157] In step 1004 the captured images undergo processing to generate detailed three-dimensional photogrammetric models. This processing may include alignment of multiple images using common reference points, generation of dense point clouds, and creation of textured mesh surfaces that accurately represent the asset's geometry. The method then implements automated feature detection algorithms to identify and map key structural elements 1006. For example, when examining storage tanks, the system may automatically detect horizontal and vertical weld lines, nozzle locations, and existing repair plates.PATENT Attorney Docket No. GROB-0028-WO

[0158] Once the base model is established, the method integrates multiple data layers onto the photogrammetric model in step 1008. Overlays may include ultrasonic thickness measurements, corrosion data, and / or remaining life calculations. The integration maintains spatial registration between the visual model and inspection data, enabling detailed understanding of asset conditions at specific locations 1010. For instance, engineers can visualize thickness measurements in relation to weld lines or examine corrosion patterns across entire asset surfaces.

[0159] In certain embodiments, multiple overlays may be presented simultaneously, and / or the user may navigate through various overlays within the workspace. For example, referencing Fig. 12, an overlay 1202 includes inspection data (e.g., thickness of an inspection surface, in the example). In the example of Fig. 12, the user can navigate through the various depicted overlays by applying an opacity to the layers. In the example of Fig. 12, an opacity interface, which may be associated with a selected overlay (or layer) can be adjusted to allow multiple layers to be depicted at the same time with comparisons between them on the overlay (e.g., indexed to physical position on the inspection surface, but any indexing parameter may be utilized such as different times, inspection operations, inspection robot configurations, inspection calibration settings, various post-processing operations to compare the effects of each, etc.). In the example of Fig. 12, overlay 1204 includes an inspection data layer or overlay compared with a photogrammetric layer or overlay. In certain embodiments, a depicted overlay can include any two or more overlays combined based on selected opacity, layer depiction order, or the like. In certain embodiments, the layer depiction order and / or opacity settings may form a part of a view, which can be shared between users and / or have limited permissions associated therewith. In certain embodiments, an overlay can include actual data, predicted data, post-processed versions of data, composite date, or the like.

[0160] In various embodiments, the method incorporates laser scanning or LIDAR data to enhance the accuracy of photogrammetric models. These additional data sources provide precise geometric measurements that complement photographic data, enabling creation of highly accurate 3D representations. The image capture process may include systematic collection from multiple angles and elevations, ensuring complete asset coverage. For example, when documenting a storage tank, images may be captured at regular intervals around the circumference and at different heights to capture surfaces and features. This comprehensive imaging approach, combined with precision measurement data, enables creation of detailed models suitable for engineering analysis.

[0161] The method may include machine learning algorithms for automated feature detection and classification. These algorithms analyze visual and geometric data to identify structural elements, surface defects, and anomalies. The system continuously improves its detection capabilities throughPATENT Attorney Docket No. GROB-0028-WO learning from validated results. For instance, when examining tank walls, the algorithms may automatically identify and classify weld lines, corrosion patterns, and structural deformations.

[0162] Embodiments include predictive analytics capabilities that leverage detailed 3D models and associated inspection data. The method may include simulation of potential failure modes based on detected features and projected degradation patterns using historical data. By aligning current models with previous documentation, the method enables quantitative analysis of changes over time.

[0163] The method may include comprehensive interactive visualization capabilities enabling detailed asset examination. Users can navigate through models in real-time, accessing specific features and associated inspection data. For example, users can zoom into areas of interest, examining detailed surface conditions while accessing linked ultrasonic measurements or inspection reports. The system integrates with operational platforms to maintain current asset documentation, automatically updating models following repairs or modifications.

[0164] The method may include quality control processes throughout model generation and analysis. Each stage of model generation may include validation steps to ensure accuracy and completeness of the final model. Laser scanning and LIDAR data provide reference measurements for validating photogrammetric reconstruction accuracy. The system may maintain detailed records of model updates, including modification history and validation results.

[0165] Embodiments of the present disclosure provide methods for optimizing field-based inspection data management and analysis. Methods may include compression and rendering capabilities to enable real-time analysis of inspection results. This approach provides particular advantages for field operations by enabling immediate validation of inspection data while maintaining the detailed information required for comprehensive asset analysis.

[0166] Referencing Fig. 11, an example flow diagram of a method 1100 improving efficiency in field-based asset inspection is schematically depicted. The example method may include specialized compression algorithms optimized for ultrasonic inspection data 1102. The compression system preserves critical signal characteristics while significantly reducing file sizes. When processing individual ultrasonic signals or hundreds or thousands of them within a bin, the system maintains signal fidelity while enabling rapid data transfer and loading. The compression algorithms may adapt to different types of ultrasonic data, including both A-scan waveforms and B-scan cross-sectional views.

[0167] The example method may include rapid rendering 1104 for compressed ultrasonic data. The rendering system enables field operators to visualize inspection results immediately after collection. For example, when examining individual bins on a storage tank, operators can view both individual A-scans and aggregated thickness measurements in real-time. The system maintainsPATENT Attorney Docket No. GROB-OQ28-WO smooth visualization performance even when handling large datasets, enabling operators to scroll through inspection results and zoom into areas of interest without delay.

[0168] In certain embodiments, the method may include analyzing inspection data 1106 as it's collected, providing immediate feedback about one or more of signal quality and validity, thickness measurements, potential anomalies or areas requiring additional inspection, or data collection coverage and completeness.

[0169] The example method may include efficient data organization strategies 1108 to support rapid retrieval and analysis. The method may include generations of index files to organize data in optimized formats that enable quick access to specific signals or measurements. The storage system maintains relationships between different data types, such as correlating ultrasonic measurements with location information and photogrammetric data.

[0170] In embodiments, the method may include generating interfaces 1110 for interacting with inspection data. Interfaces may be generated to such that operators can perform various operations including navigate through inspection results, zoom into specific areas or signals, apply different visualization filters, validate measurement accuracy, mark areas for additional inspection, and the like.

[0171] In various embodiments, the system makes multiple data layers available to users through an integrated interface. These layers may include, but are not limited to, ultrasonic thickness measurements, corrosion rates, photogrammetric surface models, operational parameters, and predicted remaining life calculations. Users can toggle between views such as raw thickness readings, difference from nominal values, and projected time to failure. For example, when examining a storage tank, users may overlay a heat map showing remaining wall thickness with projected failure times based on current corrosion rates and operating conditions.

[0172] The system enables users to interact with these layers in various ways, accessing increasingly detailed information as needed. From a high-level view showing areas requiring attention, users can drill down to examine specific readings, such as individual ultrasonic scans or detailed surface imagery. The data layers maintain spatial registration, ensuring accurate correlation between different types of measurements and observations. For instance, when selecting a location showing accelerated corrosion, users can access historical thickness trends, view surface conditions through photogrammetric imagery, and examine operational parameters that might contribute to degradation. Each layer provides context for others, enabling users to develop comprehensive understanding of asset conditions and make informed decisions about maintenance and operations.

[0173] The system also implements simulation layers that project future conditions based on current measurements and operational parameters. Users can adjust variables such as fill heights orPATENT Attorney Docket No. GROB-OQ28-WO product specifications to understand potential impacts on asset longevity. These simulation capabilities, combined with actual measurement data and visual documentation, provide powerful tools for optimizing asset management strategies while maintaining compliance with industry standards and operational requirements.

[0174] In various embodiments, the system implements a two-tier capability structure enabling flexible deployment while maintaining core functionality. The base tier provides capabilities that operate under constrained environments or limited connectivity. Embodiments enable field engineers to work with a "light stack" that includes features like basic visualization, data access, and extension deployment. This base tier ensures continuous operation even in challenging field conditions, allowing engineers to perform critical tasks without requiring full system resources.

[0175] The enhanced tier provides access to more sophisticated capabilities when conditions permit. While operating in the base tier, users maintain access to critical data and tools needed for immediate field operations. When higher-level capabilities become available, through improved connectivity or additional computational resources, users can access advanced features such as complex simulations, real-time collaboration tools, or sophisticated analysis capabilities. For example, while basic thickness mapping and visual inspection capabilities remain available in the base tier, features like remaining life calculations or complex repair simulations may require enhanced tier access.

[0176] This tiered approach supports the forward deployed engineering where field personnel need to rapidly respond to customer requirements. The base tier enables immediate action while maintaining data integrity and security, with the ability to leverage more sophisticated capabilities when available. All actions taken in the base tier automatically synchronize with the broader system when connectivity permits, ensuring consistency across operations while enabling flexible field deployment.

[0177] In various embodiments, the integration of multiple data sources and analytical capabilities provides synergistic benefits beyond their individual contributions. While components such as photogrammetric modeling, inspection data analysis, and operational simulation may operate independently, their combination enables more comprehensive asset understanding and improved decision-making capabilities. For example, photogrammetric models become more valuable when overlaid with ultrasonic thickness data, and this combined visualization becomes even more powerful when integrated with operational simulations and historical trending.

[0178] The relationship between different system components creates a multiplier effect. For example, when thickness measurements are correlated with visual surface conditions and operational parameters like fill heights and specific gravity, teams can better understand degradationPATENT Attorney Docket No. GROB-0028-WO mechanisms and optimize maintenance strategies. The integration of real-time monitoring data from fixed sensors (barnacles) with periodic inspection results and photogrammetric models enables more accurate prediction of asset behavior. While individual data sources provide valuable insights, their combination enables pattern recognition and correlation analysis that would be impossible when examining each source in isolation.

[0179] These integrations support multiple workflows across the asset management lifecycle. For instance, repair planning benefits from the combination of visual models, thickness measurements, and operational simulations to optimize intervention timing and methods. Maintenance scheduling improves through integration of historical trends, current conditions, and operational forecasts. The system's value increases as more data sources are integrated, enabling increasingly sophisticated analysis while maintaining data consistency across multiple use cases. While embodiments may implement various subsets of these capabilities, the full benefits of the system are realized through comprehensive integration of multiple data sources and analytical tools.

[0180] The described integrations enable organizations to move from discrete analysis of individual data sources to holistic asset management strategies based on comprehensive understanding of asset conditions and operational impacts. This integrated approach supports both immediate operational decisions and long-term asset management planning while maintaining consistency across different analytical workflows.

[0181] Referencing Figs. 13-20, various embodiments of the present disclosure utilize an asset focused data structure that represents industrial assets and their constituent components in a normalized, versioned model suitable for inspection, analysis, planning, repair simulation, and operational decision-making; the asset focused data structure can be utilized with any systems, platforms, apparatuses, methods, or procedures as set forth throughout the present disclosure. The embodiments of Figs. 13-20, and the related asset focused data structure, may be utilized in any embodiments throughout the present disclosure, including by providing tools that may be utilized to perform any functions therein, and / or to embody, in whole or in part, any computing devices, modules, components, or other similar aspects of various embodiments herein, and / or to implement any interfaces as depicted and described throughout the present disclosure.

[0182] The data structure may be instantiated as an “asset-model” record having a unique identifier and a type designation, with a data payload including, without limitation, a human-readable name, an effective date identifying the version’s applicability, an installation date where available, a last modified date, a version number, an identifier (including, for example, an identifier for the specific asset, and / or an identifier for an asset type and / or version, such as “TANK3.0”, and / or for any aspects thereof, such as an associated model (e.g., a wear model, operational capability model, etc.),PATENT Attorney Docket No. GROB-0028-WO a compass orientation value representing an orientation of the asset (e.g., the bearing of a line from the asset center to the x = 0 reference), an asset model type selected from a defined enumeration, a components collection describing the asset’s physical subdivisions, and / or a tags block providing organizational context such as organization, site, unit, and / or any other metadata desired. Nonlimiting examples of asset model types include vertical tanks, horizontal tanks, piping, legacy boiler assets, ships, missile silos, and legacy cones, each of which may be represented with tailored component classes and feature definitions appropriate to geometry and inspection practice.

[0183] The components collection captures the hierarchical makeup of the asset and may include, by way of example for vertical tanks, a tank shell and tank roof, while for horizontal tanks analogous shell and head components may be defined; for piping, components may include straight segments, elbows, tees, reducers, and nozzles; for legacy boiler assets, components may include tube banks, headers, drums, and membrane walls; for ships, components may include hull plating zones, frames, decks, and bulkheads; for missile silos, components may include cylindrical shells, liners, access doors, and base slabs; and for legacy cones, components may include conical shell sections and transition rings. The listed examples are for illustration and are not limiting to the present disclosure. Each component entry may include a stable component identifier, a type string selected from a component enumeration, a component name, geometric parameters such as radius, height, or other shape descriptors as applicable, a scene config with 2D placement parameters (e.g., x_2d and y_2d) to support deterministic rendering and registration, and component bounds with normalized coordinates (e.g., x_start, x_end, y_start, y_end) defining extents for visualization, indexing, and / or overlay operations.

[0184] Within each component, a features collection may define structurally and operationally relevant subregions such as courses, plates, weld seams, nozzles, stringers, stiffeners, penetrations, cutouts, or repair plates; in the tank shell example, features may include courses with y end limits and nominal specifications, and plates with feature geometry parameters (e.g., x_start, x_end, y_start, y_end) and surface specs including parameters such as: wall_nominal_thickness, coating_nominal_thickness, and train (e.g., minimum thickness within a defined region, and / or on the asset as a whole). Features may carry installation_date metadata to capture as-built versus modified states, enabling explicit representation of repairs such as patch plates or shell course replacements. Nominal specifications may be stored at either the component or feature level, for example base_thickness for a tank roof or wall_thickness for a course, and may be supplemented by additional domain-specific properties such as material grade, corrosion allowance, minimum required thickness, coating system, and weld class when present.PATENT Attorney Docket No. GROB-0028-WO

[0185] The data structure supports persistent and versioned modeling across the life of an asset by maintaining stable identifiers for components and features across versions, while the effective_date field differentiates successive versions that reflect physical changes, repairs, or reconfigurations. This approach allows inspection datasets to be associated with the correct versioned geometry, preserving historical inspections prior to a modification and aligning subsequent inspections after a modification. The structure may also maintain an index object for accelerated lookup and a generation counter for optimistic concurrency or lineage tracking. Contextual metadata in the tags block associates the model with organizational entities, sites, and units, and may be extended to include fleet identifiers, regulatory jurisdictions, and workflow tags used by permissioning and planning subsystems.

[0186] Geometric properties in the components and features collections provide deterministic spatial registration for overlays of inspection and operational data, including without limitation ultrasonic thickness measurements, corrosion mapping, photogrammetric meshes, LIDAR or laser scan point clouds, operational parameters such as fill heights and specific gravity, and computed layers such as remaining life or simulated stress distributions. The compass_orientation parameter allows consistent mapping between plant-referenced coordinates and a rendering frame, enabling aggregation of multi-source data captured at different times and by different devices. The component_bounds and feature_geometry fields define canonical extents that facilitate tiling, binning, compression, index creation, and rapid rendering of A-scan and B-scan ultrasonic data as well as other nondestructive testing modalities.

[0187] For pipes and pipeline segments, the same schema pattern may be employed with components representing linear runs, bends, tees, reducers, and flanges, while features may include girth welds, longitudinal seams, corrosion coupons, coating holidays, and repair sleeves; nominal properties may include outside diameter, wall_nominal_thickness, and tmin, along with material and coating specifications. For legacy boiler assets, tube panel components may expose tube pitch, tube count, and panel height, while features may capture ligament regions, header connection zones, tube welds, and historical repair overlays; nominal properties may include tube nominal thickness and allowable thinning thresholds. For ships, hull plating components may define strake locations and deck regions with features representing frames, stiffeners, and welded joints, and nominal properties may include plating thickness by zone and coating systems. For missile silos, cylindrical shell components may define course heights and liner plates with features describing penetrations and embedded structural members, and nominal properties may include liner thickness, concrete cover parameters (where applicable), and minimum required thickness thresholds. For legacy cones, conical shell components may define axial and circumferential subdivisions with featuresPATENT Attorney Docket No. GROB-OQ28-WO representing weld lines and localized reinforcements, and nominal properties may include base and top thickness values and included angle parameters.

[0188] The asset focused data structure may further include optional fields to associate inspection datasets, operational datasets, simulation and scenario results, repair instructions, and post-repair assessments to specific components and features, such that the system can store raw signals (e.g., A-scans), processed metrics (e.g., minimum thickness per tile), validation artifacts, and standards-based calculations alongside the geometry to which they apply. In certain embodiments, the structure supports policy- and role-aware annotations at the component or feature level, allowing users to attach notes, defect classifications, quality control confirmations, and work instructions that are permission-scoped and audit-logged by the platform. The model type enumeration may be extensible to include additional domain assets without altering the core representation, preserving a consistent contract for downstream analytics, visualization, permissions, repair planning, and scenario simulation routines.

[0189] By unifying geometry, nominal specifications, feature topology, temporal versioning, and organizational context in a single normalized representation, the asset focused data structure enables deterministic alignment of multi-layered data, efficient field rendering and validation, repeatable standards-based evaluation, and high-fidelity repair and operations planning across fleets of heterogeneous assets while maintaining backward-compatible histories of asset configuration over time.

[0190] Referencing Fig. 13, an example system includes an asset NDT platform 1301 allowing users to evaluate industrial assets, including selected groups of assets that have a relationship, for example assets of a same type, subjected to similar wear and / or operating conditions, assets of a common facility, and / or assets that operationally interact (e.g., a pipe upstream of a tank). The example platform 1301 allows users to view inspection information for the assets, to determine if the asset should be repaired, maintained, subjected to further inspection operations, or the like. In certain embodiments, the example platform 1301 allows users to determine asset remaining life, asset remaining time until maintenance and / or repair is likely to be required, and / or to plan future inspection operations including inspection types, inspection equipment setup (e.g., a robot configuration, calibrations and / or processing to be utilized, payload configurations, etc.), inspection operation descriptions (e.g., routing and / or sequencing of inspection operations to be performed), and / or asset cost / value parameters (including, for example, prospective cost / value parameters based on scenarios for asset operation, inspection, maintenance, repair, and / or probabilistic determinations simulating potential failures, operating ranges, or the like).PATENT Attorney Docket No. GROB-0028-WO

[0191] The example of Fig. 13 depicts the platform 1301 interacting with multiple types of users, including in the example using operator communications 1326 (e.g., an operator for inspection operations, for example allowing the user to confirm coverage, to retrieve annotations and / or comments from other operators that may be relevant to the inspected asset, to confirm valid data during inspection operations, to confirm and / or capture configuration information, such as for an inspection robot and / or payload, etc.), planner communications 1328 (e.g., a user preparing plans for future inspection operations, operational planning for the facility having the asset, maintenance schedules, repair schedules, etc.), analyst communications 1330 (e.g., a user analyzing inspection data and / or operational data for asset(s) and / or facilities, determining the validity and / or indicated results of inspection operations, determining best practices across a number of assets, asset types, facilities, and / or facility types, etc.), and / or customer communications 1332 (e.g., any other user interested in the asset, facility, and / or inspection data for the asset, such as an owner or operator of a facility having assets modeled on the platform). The depicted user communications of Fig. 13 are non-limiting examples to illustrate some users and / or roles of users that interact with the platform 1301. In certain embodiments, user permissions to access certain data and / or functions on the platform may be applied to individual users, classes of users, users having selected roles, or the like.

[0192] The example platform 1301 includes an asset library 1312 having a number of example or template assets that can be utilized by users, and / or users may be allowed to create new assets or asset types, and / or to modify assets from the asset library 1312, for example to prepare for inspection operations and / or planning for a particular asset or facility.

[0193] The example platform 1301 includes a user interface engine 1302 configured to exercise a user interface for users, allowing users: to create and / or modify assets; to relate assets; to create and / or modify facilities including a number of assets, and optionally relationships between assets (e.g., operational relationships, classification of assets, coordinated planning of inspections, maintenance, and / or repairs on assets, etc.); to access a visualization of assets and related data (e.g., inspection data, operational data, cost data, wear data, maintenance data, and / or repair data) before inspection operations, during inspection operations, or after inspection operations; to perform validation operations on inspection data (e.g., during inspection operations, and / or in post-processing analysis after inspection operations); and / or to provide status, notifications, and / or other communications to users, for example to alert users to off-nominal conditions and / or to detected events (e.g., set by the user or another user, and / or selected events of interest detected by the platform), and / or to provide reports to the user; and / or planning operations (e.g., inspection planning, operational planning, facility work planning such as shutdowns, repairs, production adjustments, and / or maintenance, and / or capital expenditure planning).PATENT Attorney Docket No. GROB-0028-WO

[0194] The example platform 1301 includes an asset visualization engine 1304 that allows users to view assets and / or data layers depicted with the assets (e.g., inspection data, operational data, wear data, predicted event or status data, etc.), and may include interactive operations with the user, allowing the user to change or adjust views, to selected depicted data layers, to modify the asset, to select features, positions, or any aspect of the asset for further or more detailed review. The asset displayed may be a real asset, a planned asset, and / or a modeled asset.

[0195] The example platform 1301 includes a status and reporting engine 1308, preparing views for the interface and / or communications to users that provide the users with information about ongoing inspection operations, production operations, and / or inspection data (e.g., including partial inspection data as it is created in real time). The example status and reporting engine 1308 can include communications to users of any type, including for example providing selected views and / or data layers, images from these, a link to a particular view of an asset, annotations provided automatically and / or by other users, summaries of operational and / or inspection data, and / or predictive views (e.g., the status of the asset in the future, for example as sequenced by time, production values, post-work status such as after a repair or maintenance operation, or the like).

[0196] The example platform 1301 includes an asset builder tool 1318 configured to implement the interface to allow users to create and / or modify assets, including for example assets created by another user and / or pulled from the asset library 1312. In certain embodiments, the asset builder tool 1318 allows users to store assets in the asset library 1312 (e.g., for re-use by the user or other users, and / or as a new template asset).

[0197] The example platform 1301 includes a feature analysis tool 1320 configured to implement the interface to allow users to locate, mark, and / or annotate features on the asset, where the feature is any aspect of interest such as: an identifiable feature on the asset (e.g., a bolt or rivet location, a weld line, a stair attachment, and / or an inspection identified feature such as an area exhibiting wear or corrosion). In certain embodiments, the feature analysis tool 1320 allows users to share views so a first user can view exactly the asset orientation, data layers, and / or annotations set up by a second user (and / or by the first user, for example at an earlier time). In certain embodiments, the feature analysis tool 1320 allows the user to perform certain operations relative to the feature, for example to set a coordinate system relative to the feature, to set the feature as a representative example (e.g., as a worn area, corroded area, nominal area, off-nominal area, etc., including for example what type of nominality is considered), and / or to fix the feature for high resolution position information in the area surrounding the feature.

[0198] The example platform 1301 includes a cross-asset and facility planner 1324, for example configured to implement the interface to perform analysis across multiple assets and / or across aPATENT Attorney Docket No. GROB-0028-WO facility (and / or across a group of related facilities). For example, a group of assets may be selected for statistical analysis to determine outliers within the group, and / or to determine aggregated costs for inspection, maintenance, and / or repair operations, to schedule coordinated work related to the assets (e.g., minimizing or eliminating downtime, for example when workloads among the assets may be shared, etc.). In certain embodiments, the cross-asset and facility planner 1324 allows the user to test scenarios (e.g., for inspection operations, maintenance, repair, asset replacement schedules, etc.) to minimize negative impacts (e.g., asset failures, downtime, costs for scheduled work, etc.) and / or to maximize positive impacts (e.g., compliant inspection operations, cost of operations and / or present value of the facility, maximizing production of the facility, etc.).

[0199] The example platform includes an asset library 1312 including templates, custom assets, nominal assets, or the like, allowing users to create and / or modify assets. The example platform 1301 includes an asset database 1314 including records for each asset within the platform, where the records include inspection data, relationship data between assets and / or facilities, operational data, tracking of planned or performed work, physical descriptions and / or depictions of the asset, annotations and / or views by users of the asset, features detected and / or labeled for the asset, or the like. In certain embodiments, the user interface engine 1302 allows users to search through assets (e.g., in a search tool and / or drop-down menu) and / or to filter assets (e.g., by facility, asset type, age, wear value, indicated values from inspection operations, etc.). In certain embodiments, the asset searching and / or filtering may be made based on any parameter in the asset database. The example asset database 1314 includes asset focused data structures for each asset, which may be created when an asset is utilized (e.g., within a scenario for planning inspection, maintenance, repair, replacement, and / or operational schedules and / or events), entered by an operator and / or dispatcher (e.g., during planning for and / or execution of an inspection operation), entered by an analyst (e.g., creating an asset to prepare an inspection plan, to prepare an example to illustrate analysis or other operations, etc.), and / or the asset database 1314 may include example assets that can be utilized as a starting point by users, allowing the user to conveniently build assets that are consistent and complete. In certain embodiments, asset libraries may be created for specific users, entities, facilities, or the like, allowing assets to be customized while maintaining convenience and speed for the user.

[0200] The example platform 1301 includes a facility and external data record 1316. The facility and external data record 1316 includes any information about facilities, costs, capacities, market values, external event data (e.g., weather, traffic, relevant news or social media activity, etc.) that would be of value for any operations on the platform 1301, and which may not otherwise associated with a particular asset. The example facility and external data 1316 record is a non-limiting example, and may make overall analysis and planning functions of the platform 1301 more efficientPATENT Attorney Docket No. GROB-OQ28-WO - for example compared to storing information that may be relevant to multiple assets in one place, rather than individually storing the information with each asset in the related asset record. In certain embodiments, planning for the asset and modeling of the asset, for example including the response of the asset to operations, is performed by plan and modeling engine 1310. In certain embodiments, a data conflict manager 1322 manages conflicting data, for example where multiple sources of data provide different results - for example by determining which data is more recent, from a more reliable source, eliminating data that appears to be incorrect based on previous and / or subsequent data, and / or which may be suspect (e.g., the type of data is not applicable to the asset, the data appears to be relevant to a distinct asset with a similar name, data that is collected at a time that does not appear to be correct for the asset, etc.).

[0201] Referencing Fig. 14, an example asset depicting a tank with inspection data overlaid thereon. The thresholds for color coding the asset can be selected by the user, and / or may follow criteria set up by a user such as an analyst, a facility compliance person, or the like. Figure 14 is a user interface depiction of a storage tank shell showing spatially registered wall thickness inspection data rendered as a layered overlay on the asset representation, with component boundaries and hardware features such as courses and plates labeled to provide deterministic context for measurement values. The interface presents nominal course thickness values and plate identifiers alongside a continuous thickness legend with indicated minimum and maximum readings, enabling rapid comparison of measured conditions against nominal specifications and minimum required thresholds. The view selector allows switching among data types, including thickness mapping and visual overlays, and the depiction supports alignment to photogrammetric imagery so that localized thinning, welds, and repair plates can be inspected in place on the asset model.

[0202] The controls adjacent to the visualization enable field and analyst workflows that directly support repair planning and closed-loop decision making. Options such as Immediate repairs, Outage repairs, Insert Patch, and a 5-year Arc Plan link measured degradation to actionable repair simulations and planning artifacts, accelerating repair planning and execution while aligning with cost, safety, and operational requirements. Markup and Group + Layer tools allow users to annotate findings, toggle comparative overlays, and assemble multi-layer views that combine ultrasonic thickness, nominal specifications, and visual evidence, which increases decision confidence and supports collaboration between field operators and off-site engineers in real time or near real time.

[0203] The interface elements also promote fleet-aware analytics and efficient governance.Options include an Export to Excel (or similar applications or data outputs) and plotting controls for parametric values, such as time, enable trending, benchmarking, and scenario comparisons across similar assets to identify outliers and best performers, reducing total cost of ownership through data-PATENT Attorney Docket No. GROB-0028-WO driven prioritization. Because the overlays and annotations are bound to stable component and feature identifiers within a versioned asset model, Figure 14’ s view preserves pre- and post-repair states for temporal analysis and auditability, thereby providing consistent, versioned asset histories and enhancing safety by validating proposed actions against predefined standards while issuing notifications upon deviations observed in the simulated or measured results.

[0204] Referencing Fig. 15, a detailed view of the asset of Fig. 14 is schematically depicted, for example selected by selecting a portion of the depiction of Fig. 14 for detailed analysis. Figure 15 is a user interface depiction that builds upon the visualization of Figure 14 by presenting an asset model editor opened on a detailed section of the same tank shell, enabling direct adjustment of the model geometry and feature definitions used for deterministic registration of inspection data. The editor exposes component and feature hierarchies for the selected shell section, including courses, plates, vertical welds, horizontal welds, nozzles, and fixed sensors, together with editable parameters such as y position, x start and x end bounds, nominal thickness values, and installation dates, thereby supporting versioned updates that preserve pre- and post-repair states for temporal analysis and auditability. Through the same thickness overlay used in Figure 14, the editor maintains spatial context while the user refines feature geometry or adds a repair plate, which accelerates repair planning and execution by linking measured degradation to standards-based calculations and instruction generation. The controls for Markup, Group + Layer, and Export to Excel allow analysts to assemble multi-layer comparative views, annotate proposed changes, and trend parameters such as minimum thickness over time, enabling fleet-aware benchmarking and cost-benefit prioritization aligned with operational goals. Because changes are written to a versioned asset model with stable component and feature identifiers, the interface supports closed-loop feedback — subsequent inspections can validate the impact of the edits and automatically adjust inspection cadence or maintenance schedules — while role- and action-based permissions in the editor enforce governance and compliance during model modification and publishing.

[0205] Referencing Fig. 16, a further detailed view of the asset of Fig. 14 is schematically depicted, for example selected by selecting a portion of the depiction of Fig. 15 for further detailed analysis. Figure 16 is a further detailed view of the tank shell depiction of Figure 15 and illustrates a contextual tool tip that is generated in response to a user selection or hover event over a feature of interest within the spatially registered thickness overlay. The tool tip presents deterministic, location-aware metadata and measurements including the selected component and feature identifiers such as Course 1 and Plate 1, a Remaining Corrosion or Remaining Corrosion Allowance indicator, a precise position label and value (e.g., Position: A... 0.21 in), an elevation reference (e.g., 236°), multiple proximate thickness readings (e.g., 0.56 in, 0.40 in, 0.44 in), and a Diff from Nominal indicator, allPATENT Attorney Docket No. GROB-OQ28-WO indexed to the stable component and feature geometry shown in the view. By anchoring the values to the model’s compass orientation, course boundaries, and plate extents, the figure demonstrates deterministic spatial registration that supports rapid validation of local thinning, identification of outliers relative to nominal specifications, and immediate triage of areas requiring additional inspection coverage.

[0206] The surrounding interface elements visible in Figure 16, including Marker placement, Group + Layer controls, and selectable actions such as Immediate repair Insert Patch, Outage repairs Insert Patch, and 5-year MG Plan Insert Patch, connect the point-in-time measurements to scenario planning and standards-based repair workflows. In combination with the editor capabilities of Figure 15, the tool tip enables closed-loop decision making by converting field-validated measurements into actionable steps that can be simulated, scheduled, and documented, thereby accelerating repair planning and execution while maintaining alignment with cost, safety, and operational requirements. The time-series and comparative overlays enabled by Group + Layer, together with export and reporting features, support fleet-aware benchmarking and trending, which improves decision confidence, reduces total cost of ownership through data-driven prioritization, and preserves consistent, versioned asset histories for audits and regulatory documentation.

[0207] Referencing Fig. 17, an example listing of available inspection results for an asset is schematically depicted, allowing the user to select inspection data to overlay on the asset. In certain embodiments, multiple inspection operations may be selected, for example with the visualization including a sequential depiction, a trajectory depiction, an averaged depiction, a highest- or lowest-type of depiction, or the like. Figure 17 is a user interface depiction of an inspection records tool within an inspection-based asset management platform that enables rapid discovery and organization of relevant inspection datasets across assets, facilities, sensor payloads, and field teams. The interface presents a searchable listing with controls such as Search for inspections, + Add Job, Import Inspections, + Add Inspections, and Edit, together with tabular columns indicating Unit, Component, Date, Publishing State, and a unique file and / or inspection naming for each dataset. Entries such as Facility 123 Job on Wed Jan 15 2025 and 3 White Liq Clarifier Tank Shell illustrate deterministic linkage of records to specific organizational context and component geometry, while the publishing state communicates whether a dataset is validated and available for downstream analysis. The use of unique naming allows unambiguous referencing of particular inspection payloads or robot runs, enabling the system to correlate ultrasonic, visual, or other sensor modalities with the correct asset model version.

[0208] By aggregating and filtering inspection records at the point of use, the interface advances several benefits of the present disclosure. First, it supports fleet-aware decision making by allowingPATENT Attorney Docket No. GROB-0028-WO users to rapidly benchmark comparable assets and components across facilities, and to assemble cross-asset views that feed scenario comparison and outcome analysis modules. Second, it improves collaboration and responsiveness by exposing near real-time status of ingestion and publishing, enabling field operators and off-site engineers to coordinate imports, validate coverage, and remediate errors using common identifiers and timestamps. Third, it strengthens governance and compliance through integration with role-, action-, and asset-specific permissions: the presence of publishing states and editing controls allows organizations to restrict who can import, modify, or publish datasets, while maintaining an auditable history tied to stable slugs and job identifiers.

[0209] The interface further contributes to reduced total cost of ownership and higher data quality by accelerating retrieval of the most relevant inspections for trend plotting and time-series comparisons, which in turn support optimization of inspection cadence and maintenance scheduling. Because each listed record is bound to a versioned asset model and a defined component hierarchy, subsequent planning tools can deterministically overlay measurements onto the correct geometry, preserving pre- and post-repair histories and enabling closed-loop feedback when new inspections confirm predicted outcomes. As a result, Figure 17’s tool acts as a central, permissioned gateway that streamlines field operations, elevates decision confidence, and unifies planning outputs spanning inspection plans, maintenance schedules, repair simulations, and reports.

[0210] Referencing Fig. 18, an example list of assets is depicted, for example a list of assets sharing a common feature, assets for a particular facility, etc. Figure 18 is a table view within an inspection-based asset management platform that lists assets for a facility together with available inspection metadata, enabling rapid triage and retrieval of datasets for analysis and planning. The depiction shows columnar fields including Asset, Area, Components, ERP ID, Last Inspection, and Last Updated, together with controls such as + Add Asset and a Search function, thereby linking organizational identifiers and operational context to specific model elements. Each row is bound to a versioned asset model so that selecting an entry navigates to the corresponding component and feature geometry, enabling deterministic overlay of inspection layers such as ultrasonic thickness maps, visual documentation, and computed metrics like remaining life, while preserving pre- and post-repair histories via effective dates and generation counters.

[0211] By aggregating asset-level indicators at the facility view, Figure 18 advances several benefits described in the Summary. Cross-asset fields, such as Area, Components, and Last Inspection, support fleet-aware benchmarking by surfacing outliers and best performers across similar assets, which in turn informs prioritization of inspections, maintenance, and repairs to reduce total cost of ownership. The presence of ERP ID and Last Updated ties inspection operations to enterprise records and recency of data, improving collaboration and responsiveness between fieldPATENT Attorney Docket No. GROB-0028-WO teams and off-site stakeholders and providing a single place to validate data currency before initiating scenario comparisons or generating reports. Because each asset entry is linked to a stable, versioned model, the view contributes to consistent, auditable asset histories and strengthens governance: role-, action-, and asset-specific permissions can be applied at the row or linked-record level to control data visibility, tool usage, and publishing, while audit logs capture additions, edits, and synchronization events.

[0212] The table view also accelerates planning workflows by consolidating the most salient readiness signals for each asset, enabling users to launch downstream actions — such as opening a comparative overlay, initiating a repair simulation, inserting an inspection into a cadence plan, or exporting a filtered subset for analysis — directly from the list. Trending and schedule optimization are facilitated by sortable Last Inspection and Last Updated columns, which, when combined with the component count and area context, help align inspection intensity with risk and operational needs. As a result, Figure 18 functions as a facility-level command surface that streamlines field operations, enhances decision confidence through quick access to validated information, and unifies planning outputs including inspection plans, maintenance schedules, and repair documentation.

[0213] Referencing Fig. 19, an example asset model editor is depicted, for example implemented by an asset builder tool, allowing the user to create a new asset, and / or to build a new asset by modifying an existing asset. Figure 19 is a user interface depiction of the asset model editor presenting the full tank shell in the asset view window with a spatially registered wall-thickness overlay rendered in a Diff from Nominal mode, a continuous legend indicating minima and maxima, and labeled courses, plates, welds, nozzles, fixed sensors (e.g., a barnacle), and repair plates indexed to stable component and feature identifiers and oriented by circumferential angle markers for deterministic registration; adjacent editor panels expose structured lists for Courses, Plates, Vertical Welds, Horizontal Welds, Nozzles, Marways, and Fixed Sensors with editable nominal values and geometry bounds so model updates preserve spatial alignment and version history, while controls such as Markup, Group + Layer, threshold sliders, plotting, and Export to Excel enable multi-layer comparisons, time-based trending, and rapid sharing; action shortcuts including Immediate repairs, Outage repairs, Insert Patch, and a 5-year plan connect measured degradation to simulation and instruction-generation workflows, thereby accelerating repair planning and execution, improving decision confidence through standards-aligned validation, supporting fleet-aware benchmarking and cost-benefit prioritization, and maintaining consistent, auditable pre- and post-repair states that drive closed-loop feedback and dynamic optimization of inspection cadence and maintenance scheduling.

[0214] A barnacle, as utilized herein, is a permanent or long-term sensing element affixed at a deterministic location on the asset that continuously or periodically acquires operational andPATENT Attorney Docket No. GROB-0028-WO condition data (e.g., thickness, temperature, strain, corrosion potential, vibration) and serves as a spatial and temporal anchor for multi-source dataset registration; in various embodiments, the barnacle shown in Figures 19 and 20 provides a stable reference tied to the asset’s compass orientation and feature geometry so that ultrasonic maps, photogrammetry / LIDAR overlays, historical inspections, and scenario outputs can be fused with high confidence, thereby elevating model fidelity and spatial reasoning, enabling closed-loop feedback that validates actions against observed outcomes, and improving uptime through early anomaly detection and predictive analytics. By streaming or buffering measurements between periodic robotic inspections, barnacles support optimization of inspection cadence — intensifying or deferring routes based on real-time trends — and reduce total cost of ownership by directing resources to assets and regions exhibiting the greatest risk or deviation from nominal. In addition, barnacle-indexed datasets facilitate fleet-aware benchmarking across similar features on offset assets, accelerate repair planning by correlating local thinning rates with operational conditions, and strengthen governance and auditability by anchoring versioned histories to a consistent, instrumented feature whose readings can verify data quality and confirm post-repair effectiveness over time.

[0215] Referencing Fig. 20, another example asset is depicted with visualized data. Figure 20 is a mechanical-style visualization of the tank shell similar to Figure 19, with the asset model editor closed to emphasize a clean, review-focused view that retains the spatially registered Diff from Nominal thickness overlay, continuous legend, labeled courses and plates, circumferential angle markers, and the barnacle location as a persistent reference for deterministic registration; in certain embodiments, the example of Figure 20 may be an intermediate depiction within a workflow that includes editing and planning operations such as those shown in Figure 19. By streamlining to read-only controls, the depiction supports rapid triage, time-series comparisons, and export or sharing workflows while preserving linkage to the versioned asset model, thereby enhancing decision confidence, enabling fleet-aware benchmarking, and maintaining auditable pre- and post-repair states that feed closed-loop feedback and optimization of inspection cadence and maintenance scheduling.

[0216] An example system for inspection-based asset management is described following.Aspects of the example system may be embodied by any aspects of the present disclosure, and / or aspects of the present disclosure may be embodied by any aspects of the example system.Referencing Fig. 1, an example system 100 includes a data collection component 102 configured to interpret inspection data related to an asset collected from one or more sensors 106. The inspection data can include ultrasonic thickness maps, visual imagery, corrosion measurements, temperature, pressure, and similar condition indicators gathered by fixed sensors, robotic inspection payloads, orPATENT Attorney Docket No. GROB-0028-WO handheld instruments, and interpreted by a data collection module 110. A data processing unit 122, within processing elements 112, analyzes the inspection data at an asset level 114 and at a fleet level 116, enabling both localized condition assessment and cross-asset pattern recognition. An action module 134 determines one or more actions in response to the analysis based on multiple parameters, while an asset feedback module 138 adjusts at least one parameter of the action module based on outcomes of previously determined actions for that asset, and a fleet feedback module 140 adjusts at least one parameter of the action module based on historical data from offset or similar assets, thereby closing the loop at both asset and fleet scales.

[0217] In certain embodiments, the data collection component 102 is further configured to interpret operational data related to the asset, which may include fill levels, specific gravity, temperature, pressure, and process conditions retrieved from external control systems and historian sources. The operational data may be received from a second sensor or sensor network distinct from the inspection sensor 106, for example a pressure transmitter or a permanently affixed thickness barnacle, and fused with inspection data by module 110 to enrich analytics performed by unit 122.

[0218] The action module 134 selects among actions comprising repair 126, replacement 128, maintenance 132, or enhanced inspection 130. These actions are prioritized using parameters such as asset criticality, risk tolerance, budgetary constraints, and operational impacts, and may be implemented directly or proposed to operators through interface 104. The data processing unit 122 can integrate data from one or more external operational systems 108, such as maintenance management, ERP, or process control platforms, to refine both asset-level and facility-level analysis performed by modules 114 and 116 and to improve the fidelity of action selection by module 134. Predictive analytics 120 operate in conjunction with processing unit 122 to predict future asset failures by combining historical trends and real-time sensor inputs, evaluating degradation trajectories and correlating operational stressors with observed thinning or defect growth. The action module 134 can instruct the data collection component 102 to modify the type of sensor data collected based on outcomes of previous actions, for example increasing the density of ultrasonic readings in suspect regions or deploying alternative modalities when prior measurements showed limited predictive value.

[0219] A user interface 104 allows field operators and analysts to input manual observations, contextual notes, and operational annotations which are ingested by the data processing unit 122 and incorporated into both asset-level analysis 114 and fleet-level analysis 116. The fleet feedback module 140 applies historical data from similar assets to iteratively improve parameters for newly added assets in the system, seeding initial thresholds, expected wear rates, and inspection cadences via transfer learning so that recommendations converge more quickly.PATENT Attorney Docket No. GROB-OQ28-WO

[0220] The action module 134 dynamically adjusts maintenance schedules based on real-time asset conditions and fleet-wide performance indicators, advancing or deferring interventions as supported by health metrics, trend accelerations, and opportunity windows identified by modules 114 and 116. The data processing unit 122 benchmarks asset performance against similar assets within the fleet 116 to prioritize actions across multiple assets, elevating outliers and highlighting best performers to inform resource allocation.

[0221] The data processing unit 122 includes predictive analytics that detect early-stage anomalies which may lead to asset failure, using deviation detection, residual analysis, and multi-sensor correlation to initiate targeted inspections or mitigation steps. The action module 134 performs a validation operation that compares potential outcomes of determined actions against predefined safety standards and predefined operational standards prior to authorization, ensuring compliance and risk governance; these standards may be embedded rules or retrieved from external repositories. The data collection component 102 dynamically modifies data collection frequency based on at least one of asset criticality or detected anomalies, for example intensifying monitoring on high-consequence equipment or throttling collections on stable, low-risk assets, which in turn feeds back to predictive analytics 120, asset feedback 138, and fleet feedback 140 to continuously improve decision quality.

[0222] An example system for providing a field support interface in an inspection-based asset management process is described following. Aspects of the example system may be embodied by any aspects of the present disclosure, and / or aspects of the present disclosure may be embodied by any aspects of the example system. Referencing Fig. 3, a field-accessible interface 302 enables forward-deployed personnel to access and modify system extensions managed by an extension management module 308, while a deployment module 304 deploys selected extensions and identifies requisite datasets for execution using a data access module 306. The data access module 306 retrieves asset-related inputs, which may include inspection layers such as ultrasonic thickness maps and photogrammetric models, operational parameters like fill level and temperature, utilization and type metadata, and organizational context; based on the retrieved inputs, the extension management module 308 can automatically select or recommend an appropriate extension package tailored to the asset or facility. The interface 302 supports rapid customization by field engineers without centralized information technology involvement, including parameter tuning for existing tools, addition of new analysis capabilities, and assembly of customer-specific workflows that integrate with the core platform.

[0223] To ensure stability during rapid iteration, the deployment module 304 incorporates validation tools 316 that monitor extension response time, data processing accuracy against referencePATENT Attorney Docket No. GROB-0028-WO values, and resource utilization such as CPU and memory during operation. Collaboration features 328 establish real-time or near real-time channels between field operators and off-site engineering teams, providing shared access to extension code, testing environments, and deployment status indicators so issues can be resolved quickly during development and rollout. A permissions subsystem 332 applies role -based controls that govern who may create, modify, test, and deploy extensions, including restrictions by authorization level, asset type, or facility; these controls record every modification to an extension, associating user identity and timestamps to create an auditable history.

[0224] The platform supports continuity in the field by maintaining offline data 324 and local copies of extensions, enabling continued operation when connectivity is interrupted, and synchronizing local modifications and operational data to real-time data stores 326 through a sync module 322 once connectivity is restored. A backup module 320 persists extension versions, deployment records, configuration states, and user activity logs to cloud storage to support disaster recovery and compliance requirements. During and after deployment, a feedback channel 318 provides status to the field operator, including initialization progress, data access verification, runtime performance metrics, and error notifications with suggested remediation steps; these signals can be surfaced in the flow shown in Fig. 4, where the interface is used to access data 402, create or modify extensions 404, deploy them 406, perform real-time analysis and validation 408, and update extensions based on observed results 410.

[0225] An example system for managing permissions in an inspection-based asset management and field support interface is described following. Aspects of the example system may be embodied by any aspects of the present disclosure, and / or aspects of the present disclosure may be embodied by any aspects of the example system. Referencing Fig. 5, a permission management subsystem 502 governs access to data, tools, and operations through multiple coordinated control layers that include a role-based module 504, an asset-specific module 510, and an action-based module 512, all evaluated through a multidimensional permissions matrix 508 and enforced by a validation component 506 at the time of each requested operation. The role-based module 504 assigns baseline privileges aligned to organizational responsibilities such as plant managers, site engineers, and administrators, while the action-based module 512 gates sensitive functions including data modification, extension creation and deployment, analysis tool usage and modification, and report generation. The asset-specific module 510 narrows access based on asset type, facility, and location so that users see only the components and features they are authorized to view or modify in the platforms of Figs. 1, 3, 7, 13, and related user interfaces.PATENT Attorney Docket No. GROB-0028-WO

[0226] In some embodiments, the permission management subsystem incorporates a learning engine within the role-based module 504 that analyzes historical permission patterns across assets and users to recommend permission sets for newly added assets or personnel; administrators can accept, reject, or edit these recommendations, and their feedback tunes future suggestions.Administrators may also define custom roles by cloning existing role definitions and adjusting scopes for data visibility, extension deployment capabilities, and analysis tool permissions to match site-specific governance requirements. The multidimensional permissions matrix 508 can encode action-specific restrictions such as export limitations for regulated datasets, configuration constraints for system settings, deployment constraints for field extensions, and usage boundaries for analytical workflows, and it evaluates role, action, asset, and context parameters together before authorization.

[0227] Geographic and organizational constraints may be enforced through the asset-specific module 510, which can consider facility location, regional boundaries, national jurisdictions, and corporate structures to satisfy data residency or segregation requirements while coordinating with the matrix 508 for consistent outcomes. Access rights may be dynamically updated when external factors change, including updates to regulatory requirements, organizational policy changes, shifts in operational needs, or condition changes on specific assets flagged through analyses shown in Fig. 1 (modules 114, 116, 120, 134); these changes propagate to the matrix 508 so that validation 506 reflects current policy at the moment of execution. Temporary permissions may be issued with defined activation windows, automatic expiration, scoped action limits, and documented justification, which are evaluated by validation 506 and recorded for audit. Referencing Fig. 6, an administrative workflow includes defining roles and assigning permissions 602, determining feature access based on roles 604, managing access by asset type and location 606, verifying permissions in real time 608, and updating permissions dynamically 610. An administrative interface provides real-time visualization of permission relationships, direct matrix manipulation, conflict identification, and detailed audit logging of modifications, thereby strengthening governance and compliance across the field-accessible workflows of Fig. 3 and the asset-centric views of Figs. 14-20.

[0228] An example system for providing a repair tool in an inspection-based asset management process is described following. Aspects of the example system may be embodied by any aspects of the present disclosure, and / or aspects of the present disclosure may be embodied by any aspects of the example system. Referencing Fig. 7, an example repair planning platform 712 integrates inspection and operational information through a data input module 710 that receives ultrasonic thickness measurements, corrosion data, photogrammetric models, and process parameters such as fill height, specific gravity, temperature, and pressure, and normalizes these inputs for downstream analysis. A repair planning module 708 analyzes the received datasets to identify areas requiringPATENT Attorney Docket No. GROB-OQ28-WO intervention based on industry standards and operational requirements, accounting for constraints such as proximity to weld lines and minimum patch size. A repair simulation module 706 models alternative repair approaches — such as localized patching, complete course replacement, or operational modifications — and predicts outcomes including remaining life under different scenarios by incorporating corrosion rates and operating conditions. An instruction generation module 704 produces standards-aligned work instructions that specify locations, material specifications, welding or attachment requirements, quality control procedures, and safety protocols, while a feedback integration loop 702 updates plans and models using real-time data obtained during and after repair implementation to validate effectiveness and refine future recommendations.

[0229] In operation, the platform prioritizes repair needs by evaluating operational criticality, safety implications, and utilization factors, and by calculating expected post-repair lifespan under candidate strategies using simulation outputs from module 706. For example, when a storage tank shell exhibits thinning below minimum required thickness, the system can propose a patch repair with defined dimensions and weld procedures or recommend larger-scale replacement where degradation is widespread, with the data input module 710 providing the necessary thickness and operating context and the repair planning module 708 determining feasibility given weld line proximity and standards constraints. The instraction generation module 704 embeds applicable standards, including API and regulatory requirements, into the generated procedures, and the feedback loop 702 automatically modifies instructions in response to in-process inspection findings (e.g., additional thinning detected by ultrasonic scans during surface preparation), thereby maintaining safety margins and compliance.

[0230] The platform leverages historical outcomes to improve prediction fidelity and decision quality. The repair simulation module 706 ingests prior repair records and post-repair performance to refine effectiveness and lifespan estimates for similar assets and conditions, while a cost-benefit routine within the repair planning module 708 evaluates direct repair costs, operational impacts, projected longevity improvements, and potential substitution or replacement strategies to maximize lifecycle value. An evaluation capability produces post-repair assessments that include effectiveness metrics, updated remaining life calculations, and future maintenance requirements, and a notification mechanism within the simulation pathway provides stakeholder alerts when modeled results deviate from standard parameters or indicate unexpected outcomes. A monitoring function tracks post-repair performance and issues alerts when observed metrics exceed predetermined deviation thresholds relative to projections, enabling closed-loop optimization of repair strategies and cadences. As shown in Figs. 14-16, user-facing controls such as Immediate repairs, Outage repairs, Insert Patch, and multi-layer overlays tie the repair planning, simulation, and instruction generation functions toPATENT Attorney Docket No. GROB-0028-WO spatially registered asset views, accelerating planning and execution while enhancing decision confidence and governance through versioned histories and deterministic registration.

[0231] An example system for simulating and managing inspection scenarios is described following. Aspects of the example system may be embodied by any aspects of the present disclosure, and / or aspects of the present disclosure may be embodied by any aspects of the example system. Referencing Fig. 9, a scenario simulation capability includes a simulation module 902 that generates operating and inspection scenarios 904, an outcome analysis module 910 that predicts the impacts of those scenarios on asset lifespan, performance, and failure risk, and a scenario comparison module 918 that evaluates alternative strategies side-by-side to support selection of improved approaches. A data collection module 924 acquires inputs used to parameterize and validate scenarios, drawing from sensors 926 (e.g., temperature, pressure, flow, composition, and thickness), maintenance records 928, inspection findings, and repair documentation; these inputs feed the outcome analysis module 910 to compute predicted results for each scenario, and an output module presents comparisons and summaries for operator review on the user interface (see also the platform presentation in Fig. 13). The simulation module 902 can incorporate environmental condition data — such as temperature fluctuations, humidity levels, atmospheric conditions, and seasonal variations — when generating scenarios, and may further model business condition data to evaluate tradeoffs under different market or operational contexts. Historical operational data and prior performance records are analyzed by the outcome analysis module 910 to refine predictions, improving fidelity by learning from observed degradation patterns and maintenance effectiveness.

[0232] In certain embodiments, machine learning routines within the simulation pipeline analyze real-time operational data, compare predicted outcomes against actual performance, and iteratively update prediction models and scenario parameters based on observed deviations, thereby increasing accuracy over time. The data collection module 924 can aggregate multiple information streams, including continuous sensor measurements, equipment performance logs, maintenance activity records, inspection datasets, and repair documentation, so that scenarios are grounded in comprehensive evidence. Optimization routines within the scenario comparison module 918 can prioritize strategies that increase asset lifespan while maintaining required performance levels, or, in alternative objective functions, prioritize strategies that increase facility performance subject to reliability and risk constraints. During live operation, the simulation module 902 may dynamically adjust scenario parameters in response to unexpected changes such as equipment malfunctions, process disruptions, environmental events, or resource availability shifts, ensuring that recommendations remain relevant. Based on scenario outcomes and risk / impact assessments, the outcome analysis module 910 can generate maintenance schedule recommendations that considerPATENT Attorney Docket No. GROB-OQ28-WO predicted wear patterns, projected degradation rates, resource availability, and operational impacts, which can then be surfaced to planners through the platform interfaces of Figs. 12-16 and integrated with broader planning outputs shown schematically in Fig. 13.

[0233] An example system for asset focused non-destructive testing planning is described following. Aspects of the example system may be embodied by any aspects of the present disclosure, and / or aspects of the present disclosure may be embodied by any aspects of the example system. Referencing Fig. 13, an asset NDT platform 1301 utilizes an asset focused data structure instantiated as a versioned asset-model record stored in an asset database 1314 and optionally templated from an asset library 1312. The record includes a unique identifier, a model type, a human-readable name, an effective date identifying applicability of a specific version, an installation date, a compass orientation value, a components collection, a features collection nested under each component, and tags for organizational context (e.g., organization, site, unit) that may be supplemented with fleet and jurisdiction indicators. Stable component and feature identifiers persist across versions to preserve history; an index object and generation counter support accelerated lookup and lineage. The user interface engine 1302 and asset visualization engine 1304 render the model and its overlays with deterministic spatial registration that relies on scene configuration parameters, component bounds, and feature geometry, as depicted in Figs. 14-20 where courses, plates, welds, nozzles, fixed sensors (barnacles), and repair plates are labeled and aligned to the geometry. The structure associates multi-modal datasets — such as ultrasonic A-scan / B-scan signals, processed thickness maps, photogrammetry and LIDAR meshes, operational parameters, simulation results, repair instructions, and post-repair assessments — to components and features using the stable identifiers and effective dates; examples of these overlays and associations are shown in Figs. 14-16 and 19-20.

[0234] In certain embodiments, the asset description carried by the data structure enumerates geometric parameters including radius, height, outside diameter, wall thickness, and included angle for defined components (e.g., tank shells and roofs or piping runs and elbows), with canonical extents set by component bounds and feature geometry to enable tiling and fast retrieval (Figs. 14, 15, 19). The asset description further includes nominal specifications such as wall nominal thickness, base thickness, corrosion allowance, minimum required thickness, coating nominal thickness, and material grade, which are surfaced in overlays like Diff from Nominal and remaining allowance (Figs. 15-16, 19-20). Organizational and contextual metadata is captured in tags identifying organization, site, unit, fleet identifier, regulatory jurisdiction, and applicable standards, which are used throughout the platform views (Fig. 13) and facility lists (Fig. 18). The scene configuration and bounds data include a compass orientation value, two-dimensional placement parameters for deterministic rendering, and normalized component and feature ranges, enabling aggregation of dataPATENT Attorney Docket No. GROB-0028-WO captured at different times and by different devices; this deterministic frame is illustrated by angle markers and labeled extents in Figs. 14-16 and 19-20. Lifecycle and operations metadata may include installation date, effective date, maintenance and repair history references, sensor placement maps (including barnacle locations), and calibration / validation artifacts utilized by the NDT validation engine 1306 and status / reporting engine 1308.

[0235] The asset type encompassed by the data structure may be any industrial asset represented in the platform, including, without limitation, vertical tanks, horizontal tanks, piping, legacy boiler assets, ships, missile silos, and legacy cones; components and features are tailored for each class (e.g., courses and plates for tanks; runs, bends, and girth welds for piping) and are editable via the asset builder tool 1318 and feature analysis tool 1320 (Fig. 13) and through the model editor views (Figs. 15 and 19). The data structure is further leveraged across subsystems of the platform: it registers inspection and operational datasets to component- and feature-level geometry to enable asset- and fleet-level analytics and to inform action selection such as repair, replacement, maintenance, or enhanced inspection (Fig. 1, blocks 114, 116, 124, 134, and Figs. 14-16). A field-accessible interface 302 (Fig. 3) exposes extensions that read from and write to the data structure using the stable identifiers and effective dates, enabling creation, modification, deployment, and validation of field tools without disrupting core integrity; synchronization 322, offline data 324, and real-time data 326 preserve continuity. A permissions subsystem 502 (Fig. 5) applies a multidimensional matrix of role-, action-, and asset-specific controls at component and feature granularity using the tags and stable identifiers, with validation 506 and administrative visualization for governance. A repair planning platform 712 (Fig. 7) binds standards-based calculations, simulated outcomes, and instruction generation to specific features within the data structure, versions post-repair states via effective dates, and uses feedback 702 to update future recommendations; these bindings are visualized in Figs. 14-16 and 19-20 where repair plates and candidate regions are identified. A scenario simulation capability (Fig. 9) associates inputs and results — operational parameters, inspection findings, predicted lifespans, risks, and recommended maintenance schedules — with the components and features defined in the data structure, enabling comparisons across scenarios 918 and presentation through the platform interfaces (Figs. 12-16 and 13).

[0236] A number of example procedures are described following. The example procedures may be performed by any aspects of systems of the present disclosure.

[0237] An example procedure for managing assets in an inspection-based asset management system includes: operations to receive inspection data corresponding to at least one asset; analyze the inspection data at an asset level to determine a condition of the asset; analyze the inspection data at a fleet level to identify patterns across multiple assets; determine at least one action in response to thePATENT Attorney Docket No. GROB-0028-WO analysis; execute the determined action; evaluate an outcome of the action based on updated sensor data; and adjust future asset management based on the outcome. Optional operations include selecting one or more actions from an inspection operation, an inspection schedule, a maintenance schedule, an asset utilization description, or an asset replacement description; receiving operational data corresponding to the at least one asset and analyzing the operational data at the asset and fleet levels to augment condition and pattern determinations; and evaluating the outcome by determining an asset utilization capability, an asset remaining life value, or an asset risk value.

[0238] An example procedure for managing and planning repairs in an inspection-based asset management system includes: operations to receive inspection data from an asset; analyze the data to identify areas of the asset that require repair; simulate repair actions to predict their outcomes and impact on the asset; generate repair instructions based on the repair actions; and update the repair plan dynamically based on real-time feedback from the repair site. Optional operations include specifying in the repair instructions material specifications, welding requirements, safety protocols, and quality control procedures tailored to the asset type and repair location; ranking repair actions based on operational importance, safety risk levels, and capacity utilization rates; calculating an expected post-repair asset lifespan by analyzing post-repair thickness projections, corrosion rates, and operational parameters; incorporating industry-specific standards, including API standards and regulatory requirements, into the repair instructions; automatically modifying repair instructions in response to real-time inspection data indicating changes in asset condition during repair implementation; analyzing historical repair outcome data from similar assets to refine effectiveness predictions and lifespan calculations; performing a cost-benefit analysis that considers repair costs, operational impact, projected asset longevity, asset substitution schemes, and asset replacement schemes; generating a post-repair assessment covering repair effectiveness metrics, remaining life calculations, and future maintenance requirements; generating stakeholder notifications when simulations indicate unexpected outcomes or deviations from standard repair parameters; and monitoring post-repair asset performance and issuing alerts when actual performance deviates from projections by predetermined thresholds.

[0239] An example procedure for adjusting and simulating asset inspection scenarios in an inspection-based asset management system includes: operations to collect real-time inspection data from the asset or facility; generate multiple inspection scenarios by modifying variables such as asset usage, maintenance schedules, or operational conditions; simulate outcomes of each inspection scenario, including predicted impacts on asset lifespan, performance, or risk of failure; evaluate the predicted outcomes of different inspection scenarios; and display a comparison of at least selected ones of the different inspection scenarios. Optional operations include selecting an improvedPATENT Attorney Docket No. GROB-0028-WO inspection strategy in response to the evaluation and implementing the improved strategy; continuously monitoring asset performance for deviations from predicted impacts; collecting operational data and performance metrics and using them to inform outcome predictions; incorporating environmental condition data such as temperature fluctuations, humidity levels, atmospheric conditions, and seasonal variations into scenario generation; incorporating business condition data into scenario generation; analyzing historical operational data — such as past performance metrics, maintenance records, and observed degradation patterns — to refine scenario predictions; implementing machine learning algorithms that analyze real-time operational data, compare predicted and actual performance, refine models based on observed deviations, and adjust scenario parameters based on learned patterns; gathering operational data including continuous sensor measurements, equipment performance logs, maintenance activity records, inspection findings, and repair documentation; applying optimization algorithms that prioritize scenarios increasing asset lifespan while maintaining required performance levels; applying optimization algorithms that prioritize scenarios increasing facility performance while maintaining required performance levels; dynamically adjusting scenarios in response to unexpected operational changes including equipment malfunctions, process disruptions, environmental events, or resource availability changes; and generating maintenance schedule recommendations based on predicted wear patterns, projected degradation rates, resource availability, and operational impact assessments.

[0240] An example procedure for using photogrammetry in inspection-based asset management includes: operations to capture images of an asset using cameras or sensors; obtain an existing 3D model of the asset; detect key features of the asset, including structural elements such as welds, cracks, and nozzles; overlay the images onto the 3D model for visualization of asset condition; and use the 3D model to guide decision-making in asset management, maintenance, or repair planning. Optional operations include integrating the detected key features to inform asset- and fleet-level analyses; analyzing key features to derive condition assessments and cross-asset patterns; determining and selecting actions including repair, replacement, maintenance, or enhanced inspection operations based on detected key features; utilizing detected key features as contextual data for inspection analysis or inspection scenario prediction; enabling operators to input manual observations on the 3D model for visualization of asset condition; determining asset similarity in response to detected key features; determining anomalies associated with the asset in response to detected key features; implementing a user interface and detecting key features in response to user interactions, including allowing users to define key feature criteria based on permissions; treating obstacles or inspection data patterns as key features; optimizing inspection scenarios in response to detected key features; integrating LiDAR or laser scanning with detected key features to enhancePATENT Attorney Docket No. GROB-OQ28-WO geometric accuracy for inspection analysis; utilizing detected key features to align multiple inspection datasets on the asset; overlaying inspection data onto the 3D model for condition visualization while optionally creating the 3D model using integrated laser scanning or LIDAR; implementing machine learning algorithms to automatically classify structural elements, identify surface defects and anomalies, label detected features by type and severity, and improve through continuous learning from validated results; collecting image data from multiple angles and elevations to ensure complete coverage — including distinct circumferential positions, vertical positions at different heights, and detailed views of key features; simulating potential failure modes based on detected key features, projecting degradation patterns using overlaid inspection data, and estimating remaining life under different operational scenarios; performing temporal analyses to align current and historical models, quantify geometric changes over time, track degradation patterns, and document repair effectiveness; providing interactive visualization that supports real-time navigation, zoom, access to linked inspection data and reports, and extraction of measurements from the model; and integrating with operational systems to automatically update models following repairs, incorporate maintenance activity records, reflect current asset configurations, and maintain accurate digital representations of physical asset condition.

[0241] An example procedure for field-based asset inspection and data management includes: operations to compress A-scan and B-scan data into optimized file formats for fast loading and rendering; render the compressed A-scan and B-scan; analyze the data using a real-time processing module to provide instant feedback on asset condition; organize and store the data in a format that facilitates quick retrieval for further analysis or reporting; and provide field operators with a user interface to manipulate and inspect the rendered data in real time. Optional operations include implementing automated validation algorithms that assess signal quality in real time, identify potential measurement anomalies, verify data collection coverage, and alert operators to areas requiring additional inspection or validation; employing optimized file formats that utilize indexed storage structures for rapid signal access, hierarchical data organization, compressed waveform data, and integrated location information; and enabling interface functions for real-time manipulation of visualization parameters, comparison of current readings against historical data, annotation of inspection findings, and initiation of additional data collection.

[0242] An example procedure for providing field support in an inspection-based asset management system includes: operations to implement a field support interface that allows access, creation, or modification of system extensions via the interface; deploy the created or modified extensions to the system; perform real-time data analysis on inspection data of an asset within the field interface; and update extensions based on real-time requirements. Optional operations include validating at least aPATENT Attorney Docket No. GROB-OQ28-WO portion of the inspection data as part of the real-time data analysis within the field interface, and / or retrieving operational data of the asset and performing the real-time data analysis on the retrieved operational data within the field interface.

[0243] An example procedure for managing permissions in an inspection-based asset management system includes: operations to define user roles and assign a corresponding set of permissions to each role; determine access to system features based on the role of the user; manage access to asset data and operational tools based on asset type, asset location, and user role; verify user permissions in real time when an action is requested; and update and modify the set of permissions dynamically based on changes in user roles, system actions, or asset characteristics. Optional operations include defining user roles to include at least one of an operator user role, an analyst user role, or a facility user role: and storing the asset data and the asset characteristics within an asset focused data structure to enable role-, action-, and asset-specific controls at component and feature granularity.

[0244] The methods and systems described herein may be deployed in part or in whole through a machine having a computer, computing device, processor, circuit, and / or server that executes computer readable instructions, program codes, instructions, and / or includes hardware configured to functionally execute one or more operations of the methods and systems disclosed herein. The terms computer, computing device, processor, circuit, and / or server, as utilized herein, should be understood broadly.

[0245] Any one or more of the terms computer, computing device, processor, circuit, and / or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and / or server. Additionally or alternatively, a computer, computing device, processor, circuit, and / or server may be a separate hardware device, one or more computing resources distributed across hardware devices, and / or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and / or output devices, network and / or communication resources, memory resources of any type, processing resources of any type, and / or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein.

[0246] Network and / or communication resources include, without limitation, local area network, wide area network, wireless, internet, or any other known communication resources and protocols. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers include, without limitation, a general purpose computer, a server, an embedded computer, a mobile device, a virtual machine, and / or an emulated version of one or more of these. Example andPATENT Attorney Docket No. GROB-0028-WO non-limiting hardware, computers, computing devices, processors, circuits, and / or servers may be physical, logical, or virtual. A computer, computing device, processor, circuit, and / or server may be a distributed resource included as an aspect of several devices; and / or included as an interoperable set of resources to perform described functions of the computer, computing device, processor, circuit, and / or server, such that the distributed resources function together to perform the operations of the computer, computing device, processor, circuit, and / or server. In certain embodiments, each computer, computing device, processor, circuit, and / or server may be on separate hardware, and / or one or more hardware devices may include aspects of more than one computer, computing device, processor, circuit, and / or server, for example as separately executable instructions stored on the hardware device, and / or as logically partitioned aspects of a set of executable instructions, with some aspects of the hardware device comprising a part of a first computer, computing device, processor, circuit, and / or server, and some aspects of the hardware device comprising a part of a second computer, computing device, processor, circuit, and / or server.

[0247] A computer, computing device, processor, circuit, and / or server may be part of a server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions, and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor, or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions, and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and the like.PATENT Attorney Docket No. GROB-0028-WO

[0248] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

[0249] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer readable instructions on a server, client, firewall, gateway, hub, router, or other such computer and / or networking hardware. The computer readable instructions may be associated with a server that may include a file server, print server, domain server, internet server, intranet server and other variants such as secondary server, host server, distributed server, and the like. The server may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0250] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of instructions across the network. The networking of some or all of these devices may facilitate parallel processing of program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the server through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.

[0251] The methods, program code, instructions, and / or programs may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client, and the like. The client may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, program code, instructions, and / or programs as described herein and elsewhere may be executed by the client. In addition, other devices utilized for execution of methodsPATENT Attorney Docket No. GROB-OQ28-WO as described in this application may be considered as a part of the infrastructure associated with the client.

[0252] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of methods, program code, instructions, and / or programs across the network. The networking of some or all of these devices may facilitate parallel processing of methods, program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the client through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.

[0253] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules, and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM, and the like. The methods, program code, instructions, and / or programs described herein and elsewhere may be executed by one or more of the network infrastructural elements.

[0254] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like.

[0255] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players, and the like. These mobile devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute methods, program code, instructions, and / or programs stored thereon.Alternatively, the mobile devices may be configured to execute instructions in collaboration withPATENT Attorney Docket No. GROB-OQ28-WO other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute methods, program code, instructions, and / or programs. The mobile devices may communicate on a peer to peer network, mesh network, or other communications network. The methods, program code, instructions, and / or programs may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store methods, program code, instructions, and / or programs executed by the computing devices associated with the base station.

[0256] The methods, program code, instructions, and / or programs may be stored and / or accessed on machine readable transitory and / or non-transitory media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

[0257] Certain operations described herein include interpreting, receiving, and / or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and / or determining any value parameter, input, data, and / or other information include, without limitation; receiving data via a user input: receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and / or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value. For example, when communications are down, intermittent, or interrupted, a first operation to interpret, receive, and / or determine a data value may be performed, and when communications are restored an updated operation to interpret, receive, and / or determine the data value may be performed.

[0258] Certain logical groupings of operations herein, for example methods or procedures of the current disclosure, are provided to illustrate aspects of the present disclosure. Operations describedPATENT Attorney Docket No. GROB-OQ28-WO herein are schematically described and / or depicted, and operations may be combined, divided, reordered, added, or removed in a manner consistent with the disclosure herein. It is understood that the context of an operational description may require an ordering for one or more operations, and / or an order for one or more operations may be explicitly disclosed, but the order of operations should be understood broadly, where any equivalent grouping of operations to provide an equivalent outcome of operations is specifically contemplated herein. For example, if a value is used in one operational step, the determining of the value may be required before that operational step in certain contexts (e.g. where the time delay of data for an operation to achieve a certain effect is important), but may not be required before that operation step in other contexts (e.g. where usage of the value from a previous execution cycle of the operations would be sufficient for those purposes). Accordingly, in certain embodiments an order of operations and grouping of operations as described is explicitly contemplated herein, and in certain embodiments re-ordering, subdivision, and / or different grouping of operations is explicitly contemplated herein.

[0259] The methods and systems described herein may transform physical and / or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.

[0260] The elements described and depicted herein, including in flow charts, block diagrams, and / or operational descriptions, depict and / or describe specific example arrangements of elements for purposes of illustration. However, the depicted and / or described elements, the functions thereof, and / or arrangements of these, may be implemented on machines, such as through computer executable transitory and / or non-transitory media having a processor capable of executing program instructions stored thereon, and / or as logical circuits or hardware arrangements. Example arrangements of programming instructions include at least: monolithic structure of instructions; standalone modules of instructions for elements or portions thereof; and / or as modules of instructions that employ external routines, code, services, and so forth; and / or any combination of these, and all such implementations are contemplated to be within the scope of embodiments of the present disclosure Examples of such machines include, without limitation, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements described and / or depicted herein, and / or any other logical components, may be implemented on a machine capable of executing program instructions. Thus, while the foregoing flow charts, block diagrams, and / or operational descriptions set forth functional aspects of the disclosed systems, any arrangementPATENT Attorney Docket No. GROB-0028-WO of program instructions implementing these functional aspects are contemplated herein. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein.Additionally, any steps or operations may be divided and / or combined in any manner providing similar functionality to the described operations. All such variations and modifications are contemplated in the present disclosure. The methods and / or processes described above, and steps thereof, may be implemented in hardware, program code, instructions, and / or programs or any combination of hardware and methods, program code, instractions, and / or programs suitable for a particular application. Example hardware includes a dedicated computing device or specific computing device, a particular aspect or component of a specific computing device, and / or an arrangement of hardware components and / or logical circuits to perform one or more of the operations of a method and / or system. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium.

[0261] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and computer readable instructions, or any other machine capable of executing program instructions.

[0262] Thus, in one aspect, each method described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or computer-readable instructions described above. All such permutations and combinations are contemplated in embodiments of the present disclosure.PATENT Attorney Docket No. GROB-OQ28-WO

[0263] While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples, but is to be understood in the broadest sense allowable by law.

Claims

PATENT Attorney Docket No. GROB-0028-WO What is claimed is:

1. A system for an inspection-based asset management, comprising:a data collection component configured to interpret inspection data related to an asset collected from a sensor;a data processing unit configured for analyzing the inspection data at an asset level and at a fleet level;an action module configured for determining at least one action in response to the analysis based on a plurality of parameters;an asset feedback module configured to adjust at least one parameter of the action module based on outcomes of previously determined actions; anda fleet feedback module configured to adjust at least one parameter of the action module based on historical data.

2. The system of claim 1, wherein the data collection component is further configured to interpret operational data related to the asset.

3. The system of claim 2, wherein the data collection component is further configured to interpret the operational data from a second sensor.

4. The system of claim 1, wherein the action module is configured to select actions from repair, replacement, maintenance, or inspection of the asset.

5. The system of claim 1, wherein the data processing unit is further configured to integrate data from at least one external operational system to refine the asset-level and facility-level analysis.

6. The system of claim 1, wherein the data processing unit is further configured to predict future asset failures based on a combination of historical data and real-time sensor data.

7. The system of claim 1, wherein the action module is configured to modify a type of sensor data collected based on outcomes of previous actions.

8. The system of claim 1, further comprising a user interface configured to receive manual operator input of additional operational data, wherein the data processing unit incorporates the manual operator input into the asset-level and fleet-level analysis.

9. The system of claim 1, wherein the fleet feedback module is configured to apply historical data from similar assets to iteratively improve parameters for newly added assets in the system.

10. The system of claim 1, wherein the action module is configured to dynamically adjust maintenance schedules based on real-time asset conditions and fleet-wide performance indicators.

11. The system of claim 1, wherein the data processing unit is configured to benchmark asset performance against similar assets within the fleet to prioritize actions across multiple assets.PATENT Attorney Docket No. GROB-0028-WO 12. The system of claim 1, wherein the data processing unit includes predictive analytics configured to detect early-stage anomalies that could lead to asset failure.

13. The system of claim 1, wherein the action module is configured to perform a validation operation to compare potential outcomes of determined actions against at least one of predefined safety standards or predefined operational standards.

14. The system of claim 1, wherein the data collection component is configured to dynamically modify data collection frequency based on at least one of asset criticality or detected anomalies.

15. A method for managing assets in an inspection-based asset management system, the method comprising:receiving inspection data corresponding to at least one asset;analyzing the inspection data at an asset level to determine a condition of the asset; analyzing the inspection data at a fleet level to identify patterns across multiple assets; determining at least one action in response to the analysis;executing the determined action;evaluating an outcome of the action based on updated sensor data; andadjusting future asset management based on the outcome.

16. The method of claim 15, wherein the determined action comprises at least one action selected from: an inspection operation, an inspection schedule, a maintenance schedule, an asset utilization description, or an asset replacement description.

17. The method of claim 15, further comprising:receiving operational data corresponding to the at least one asset;analyzing the operational data at the asset level to determine the condition of the asset; and analyzing the operational data at the fleet level to identify patterns across the multiple assets.

18. The method of claim 15, evaluating the outcome comprises at least one of: determining an asset utilization capability;determining an asset remaining life value; ordetermining an asset risk value.

19. A system for providing a field support interface in an inspection-based asset management process, comprising:a field-accessible interface for accessing and modifying system extensions;an extension management module configured to allow creation, modification, or deployment of extensions; andPATENT Attorney Docket No. GROB-0028-WO a deployment module for deploying extensions in response to selection from the field-accessible interface, wherein the deployment module identifies data for the deployed extension.

20. The system of claim 19, further comprising a data access module for retrieving asset data, and wherein the extension management module is further configured to select an extension package in response to the asset data.

21. The system of claim 20, wherein the asset data comprises at least one of: inspection data for the asset:operational data for the asset;asset utilization data;asset type data; orentity data for an entity related to the asset.

22. The system of claim 19, wherein the field-accessible interface enables customization of extensions by forward deployed engineers without requiring centralized information technology support, the customization includes at least one of modification of extension parameters, addition of new analysis tools, or creation of customer-specific workflows.

23. The system of claim 19, wherein the deployment module comprises field validation tools configured to analyze extension performance in real-time, the validation tools measuring at least one of extension response times, data processing accuracy, or system resource utilization during operation.

24. The system of claim 19, further comprising a collaboration module configured to establish real-time or near real-time communication channels between field operators and off-site engineers during extension modification and deployment, the collaboration module enabling shared access to at least one of extension code, testing results, or deployment status information.

25. The system of claim 19, wherein the extension management module implements rolebased permissions controlling extension creation and modification capabilities.

26. The system of claim 25, wherein the permissions comprise at least one of: restricting extension development access based on user role;restricting extension development access based on user authorization level;limiting deployment capabilities to specific asset types; orlimiting deployment capabilities to specific facilities.

27. The system of claim 25, wherein the extension management module is configured to maintain records of extension modifications.PATENT Attorney Docket No. GROB-0028-WO 28. The system of claim 27, wherein the extension management module is further configured to associate at least one of a user identification or timestamp data with the records of extension modifications.

29. The system of claim 19, wherein the field-accessible interface supports offline extension functionality, the system being configured to: maintain local copies of extension code and required data; enable extension operation without internet connectivity; and automatically synchronize extension modifications and operational data when internet connectivity is restored.

30. The system of claim 19, further comprising a backup module configured to automatically store extension modifications and associated data changes to a cloud storage solution, the backup module maintaining modification history, deployment records, and user activity logs.

31. The system of claim 19, wherein the deployment module provides real-time feedback to field operators regarding extension deployment status, the feedback comprising at least one of: extension initialization status;data access verification;performance metrics during operation;error notifications; orerror notifications with suggested remediation steps.

32. A method for providing field support in an inspection-based asset management system, comprising:implementing a field support interface, thereby allowing access, creation, or modification of system extensions via the interface;deploying the created or modified extensions to the system;performing real-time data analysis on inspection data of an asset within the field interface; andupdating extensions based on real-time requirements.

33. The method of claim 32, wherein the performing real-time data analysis comprises validating at least a portion of the inspection data.

34. The method of claim 32, further comprising:retrieving operational data of the asset; andperforming the real-time data analysis on the operational data.

35. A system for managing permissions in an inspection-based asset management and field support interface, comprising:a role-based permissions management module configured to assign and manage permissions for users based on their roles;PATENT Attorney Docket No. GROB-0028-WO an action-based permissions module for controlling access to specific actions within the system;an asset-specific permissions module for granting access to data and tools based on asset type and location;a multidimensional permissions matrix that combines role, action, and asset type to provide control over system functionalities; anda permissions validation module that verifies user permissions based on the permission matrix.

36. The system of claim 35, wherein the role-based permissions management module comprises a learning engine configured to dynamically determine permissions based on established permission patterns, the learning engine analyzing historical permission assignments across multiple assets to recommend new permission allocations.

37. The system of claim 36, wherein the action-based permissions module controls specific system actions comprising:data modification capabilities;extension creation and deployment rights:analysis tool access;analysis tool modification; orreport generation permissions.

38. The system of claim 35, wherein the role-based permissions management module enables administrators to create and modify custom roles, the custom roles comprising:unique combinations of system access rights;specific data visibility levels;customized extension deployment capabilities; ordefined analysis tool permissions.

39. The system of claim 35, wherein the multidimensional permissions matrix implements action-specific restrictions including at least one of:data export limitations:system configuration controls:extension deployment constraints; oranalysis tool usage boundaries.

40. The system of claim 35, wherein the asset-specific permissions module implements geographic-based access controls, the controls determining data and tool access based on at least one of facility location, regional boundaries, national jurisdictions, or corporate organizational structures.PATENT Attorney Docket No. GROB-0028-WO 41. The system of claim 35, wherein the multidimensional pemiissions matrix dynamically updates access rights based on external factors comprising at least one of:asset condition changes;operational requirement modifications;regulatory requirement updates; ororganizational policy changes.

42. The system of claim 35, wherein the role-based permissions management module enables assignment of temporary permissions, the temporary permissions comprising at least one of:defined activation periods;automatic expiration parameters:specific action limitations; ordocumented justification requirements.

43. The system of claim 35, further comprising an administrative interface configured to enable real-time visualization and modification of the multidimensional permissions matrix, the interface providing:graphical permission relationship displays;direct matrix manipulation capabilities;permission conflict identification; andmodification audit logging.

44. A method for managing permissions in an inspection-based asset management system, the method comprising:defining user roles and assigning a set of permissions to each role;determining access to system features based on the role of the user;managing access to asset data and operational tools based on asset type, asset location, and user role;verifying the permissions of each user in real-time when an action is requested; and updating and modifying the set of permissions dynamically based on changes in user roles, system actions, or asset characteristics.

45. The method of claim 44, wherein the user roles comprise at least one role selected from: an operator user role;an analyst user role; ora facility user role.

46. The method of claim 44, further comprising storing the asset data and the asset characteristics in an asset focused data structure.PATENT Attorney Docket No. GROB-0028-WO 47. A system for providing a repair tool in an inspection-based asset management process, comprising:a data input module configured to receive operational and inspection data related to an asset’s condition;a repair planning module for identifying areas of the asset that require repair based on the operational and inspection data;a repair simulation module for modeling and predicting outcomes of various repair actions on the asset;an instruction generation module for producing repair instructions;a feedback loop configured to adjust a repair plan based on real-time data during or after the repair.

48. The system of claim 47, wherein the instruction generation module produces repair instructions comprising material specifications, welding requirements, safety protocols, and quality control procedures specific to an asset type and repair location.

49. The system of claim 47, wherein the repair planning module implements a prioritization algorithm that ranks repair actions based on asset criticality factors comprising operational importance, safety risk levels, or capacity utilization rates.

50. The system of claim 47, wherein the repair simulation module calculates expected asset lifespan following repair implementation by analyzing post-repair thickness projections, corrosion rates, or operational parameters.

51. The system of claim 47, wherein the instruction generation module incorporates industryspecific standards comprising API standards, regulatory requirements, and operational guidelines specific to each repair type and asset classification.

52. The system of claim 47, wherein the feedback loop automatically modifies repair instructions in response to real-time inspection data indicating changes in asset condition during repair implementation.

53. The system of claim 47, wherein the repair simulation module analyzes historical repair outcome data from similar assets to refine repair effectiveness predictions and lifespan calculations.

54. The system of claim 47, wherein the repair planning module implements cost-benefit analysis to prioritize repairs that maximize operational lifecycle value, considering factors comprising repair costs, operational impact, projected asset longevity, an asset substitution scheme, or an asset replacement scheme.PATENT Attorney Docket No. GROB-0028-WO 55. The system of claim 47, further comprising an evaluation module configured to generate post-repair assessments comprising repair effectiveness metrics, remaining life calculations, and future maintenance requirements.

56. The system of claim 47, wherein the repair simulation module generates stakeholder notifications when simulation results indicate unexpected outcomes or deviations from standard repair parameters.

57. The system of claim 47, further comprising a monitoring module configured to track postrepair asset performance and generate alerts when actual performance deviates from projected metrics by predetermined thresholds.

58. A method for managing and planning repairs in an inspection-based asset management system, the method comprising:receiving inspection data from an asset;analyzing the data to identify areas of the asset that require repair;simulating repair actions to predict their outcomes and impact on the asset;generating repair instructions based on the repair actions; andupdating the repair plan dynamically based on real-time feedback from repair site.

59. The method of claim 58, wherein generating the repair instructions comprises specifying material specifications, welding requirements, safety protocols, and quality control procedures tailored to an asset type and a repair location.

60. The method of claim 58, wherein analyzing the data to identify areas of the asset that require repair further comprises ranking repair actions based on factors comprising operational importance, safety risk levels, and capacity utilization rates.

61. The method of claim 58, wherein simulating the repair actions to predict their outcomes further comprises calculating an expected post-repair asset lifespan by analyzing post-repair thickness projections, corrosion rates, and operational parameters.

62. The method of claim 58, wherein generating the repair instructions comprises incorporating industry-specific standards including API standards, regulatory requirements, and operational guidelines specific to a repair type and an asset classification.

63. The method of claim 58, further comprising automatically modifying the repair instructions in response to real-time inspection data indicating changes in asset condition during repair implementation.

64. The method of claim 58, wherein simulating the repair actions further comprises analyzing historical repair outcome data from similar assets to refine repair effectiveness predictions and asset lifespan calculations.PATENT Attorney Docket No. GROB-0028-WO 65. The method of claim 58. wherein analyzing the data to identify areas of the asset that require repair further comprises performing a cost-benefit analysis to prioritize repairs that maximize operational lifecycle value, the cost-benefit analysis considering repair costs, operational impact, projected asset longevity, an asset substitution scheme, and an asset replacement scheme.

66. The method of claim 58, further comprising generating a post-repair assessment including repair effectiveness metrics, remaining life calculations, and future maintenance requirements.

67. The method of claim 58, wherein simulating the repair actions further comprises generating stakeholder notifications when simulation results indicate unexpected outcomes or deviations from standard repair parameters.

68. The method of claim 58, further comprising monitoring post-repair asset performance and generating alerts when actual performance deviates from projected metrics by predetermined thresholds.

69. A system for simulating and managing inspection scenarios, comprising:a data collection module configured to gather inspection data and performance metrics related to an asset:a simulation module configured to create multiple inspection scenarios by adjusting asset operations, including at least one of inspection schedules, asset usage, maintenance schedules, or operational conditions:an outcome analysis module configured to predict results of various inspection adjustments, including impacts on at least one of an asset lifespan, performance, or risk of failure;a scenario comparison module configured to evaluate the predicted outcomes of different inspection scenarios;an output module configured to display the comparison of at least selected ones of the different inspection scenarios.

70. The system of claim 69, further comprising:wherein the data collection module is further configured to gather operational data and performance metrics related to the asset; andwherein the outcome analysis module is further configured to predict the results in response to the operational data and performance metrics.

71. The system of claim 70, wherein the simulation module incorporates environmental condition data comprising temperature fluctuations, humidity levels, atmospheric conditions, or seasonal variations when generating operational scenarios.PATENT Attorney Docket No. GROB-0028-WO 72. The system of claim 70, wherein the simulation module incorporates business condition data when generating inspection scenarios.

73. The system of claim 69, wherein the outcome analysis module analyzes historical operational data to refine scenario predictions, the historical data comprising past performance metrics, maintenance records, or observed degradation patterns under similar operating conditions.

74. The system of claim 70, wherein the simulation module implements machine learning algorithms configured to:analyze real-time operational data;compare predicted outcomes against actual performance;refine prediction models based on observed deviations; andadjust scenario parameters based on learned patterns.

75. The system of claim 70, wherein the data collection module gathers operational data comprising at least one of:continuous sensor measurements;equipment performance logs;maintenance activity records;inspection findings; orrepair documentation.

76. The system of claim 69, wherein the scenario comparison module implements optimization algorithms that prioritize inspection scenarios increasing asset lifespan while maintaining required performance levels.

77. The system of claim 69, wherein the scenario comparison module implements optimization algorithms that prioritize inspection scenarios increasing facility performance while maintaining required performance levels.

78. The system of claim 69, wherein the simulation module dynamically adjusts scenarios in response to unexpected operational changes comprising:equipment malfunctions;process disruptions;environmental events; orresource availability changes.

79. The system of claim 69, wherein the outcome analysis module generates maintenance schedule recommendations based on:predicted wear patterns;projected degradation rates;PATENT Attorney Docket No. GROB-0028-WO resource availability; andoperational impact assessments.

80. A method for adjusting and simulating asset inspection scenarios in an inspection-based asset management system, the method comprising:collecting real-time inspection data from the asset or facility;generating multiple inspection scenarios by modifying variables such as asset usage, maintenance schedules, or operational conditions;simulating outcomes of each inspection scenario, including predicted impacts on asset lifespan, performance, or risk of failure;evaluating the predicted outcomes of different inspection scenarios; anddisplaying a comparison of at least selected ones of the different inspection scenarios.

81. The method of claim 80, further comprising selecting an improved inspection strategy in response to the evaluating, and implementing the improved inspection strategy.

82. The method of claim 81, further comprising continuously monitoring asset performance for deviation from the predicted impacts on asset lifespan, performance, or risk of failure.

83. The method of claim 80, further comprising collecting operational data and performance metrics related to the asset, and wherein predicting the outcomes of the inspection scenarios is performed in response to the collected operational data and performance metrics.

84. The method of claim 80, wherein generating the inspection scenarios further comprises incorporating environmental condition data comprising temperature fluctuations, humidity levels, atmospheric conditions, or seasonal variations.

85. The method of claim 80, wherein generating the inspection scenarios further comprises incorporating business condition data.

86. The method of claim 80, further comprising analyzing historical operational data to refine scenario predictions, the historical operational data comprising past performance metrics, maintenance records, or observed degradation patterns under similar operating conditions.

87. The method of claim 80, further comprising implementing machine learning algorithms configured to analyze real-time operational data, compare predicted outcomes against actual performance, refine prediction models based on observed deviations, and adjust scenario parameters based on learned patterns.

88. The method of claim 80, wherein collecting real-time inspection data further comprises gathering operational data comprising at least one of continuous sensor measurements, equipment performance logs, maintenance activity records, inspection findings, or repair documentation.PATENT Attorney Docket No. GROB-0028-WO 89. The method of claim 80. further comprising applying optimization algorithms that prioritize inspection scenarios increasing asset lifespan while maintaining required performance levels.

90. The method of claim 80, further comprising applying optimization algorithms that prioritize inspection scenarios increasing facility performance while maintaining required performance levels.

91. The method of claim 80, wherein generating the inspection scenarios further comprises dynamically adjusting scenarios in response to unexpected operational changes comprising equipment malfunctions, process disruptions, environmental events, or resource availability changes.

92. The method of claim 80, further comprising generating maintenance schedule recommendations based on predicted wear patterns, projected degradation rates, resource availability, and operational impact assessments.

93. A method for using photogrammetry in inspection-based asset management, the method comprising:capturing images of an asset using cameras or sensors;obtaining an existing 3D model of the asset;detecting key features of the asset, including structural elements such as welds, cracks, and nozzles;overlaying the images onto the 3D model for visualization of asset condition; and using the 3D model to guide decision-making in asset management, maintenance, or repair planning.

94. The method of claim 93, further comprising integrating the detected key features to inform asset and fleet-level analyses.

95. The method of claim 93, further comprising analyzing the detected key features to derive condition assessments and cross-asset patterns.

96. The method of claim 93, further comprising determining and selecting actions including repair, replacement, maintenance, or enhanced inspection operations based on the detected key features.

97. The method of claim 93, further comprising utilizing the detected key features as contextual data for at least one of an inspection analysis or an inspection scenario prediction.

98. The method of claim 93, further comprising enabling an operator to input manual observations on the 3D model for visualization of the asset condition.

99. The method of claim 93, further comprising determining a similarity of assets in response to the detected key features.PATENT Attorney Docket No. GROB-0028-WO 100. The method of claim 93, further comprising determining an anomaly associated with the asset in response to the detected key features.

101. The method of claim 93, further comprising implementing a user interface, and detecting the key features in response to user interactions on the user interface.

102. The method of claim 101, further comprising allowing the user to define key feature criteria in response to permissions associated with the user.

103. The method of claim 93, wherein the key features further include at least one of an obstacle or an inspection data pattern.

104. The method of claim 93, further comprising optimizing inspection scenarios in response to the detected key features.

105. The method of claim 93, further comprising integrating LiDAR or laser scanning with the detected key features to enhance geometric accuracy for inspection analysis.

106. The method of claim 93, further comprising utilizing the detected key features to align multiple inspection data sets on the asset.

107. The method of claim 93, further comprising overlaying inspection data onto the 3D model for visualization of asset condition.

108. The method of claim 107, wherein creating the 3D model comprises integrating laser scanning or LIDAR data to enhance geometric accuracy and spatial resolution of the model.

109. The method of claim 93, wherein detecting key features comprises implementing machine learning algorithms configured to:automatically classify structural elements;identify surface defects and anomalies;label detected features according to type and severity; andimprove detection accuracy through continuous learning from validated results.

110. The method of claim 93, wherein capturing images comprises collecting image data from multiple angles and elevations to ensure complete asset coverage and optimal photogrammetric reconstruction.

111. The method of claim 110, collecting image data from multiple angles and elevations comprises collecting data from at least one of:distinct circumferential positions around the asset;distinct vertical positions at different heights; ordetailed views of one or more key features.

112. The method of claim 93, further comprising implementing predictive analytics to: simulate potential failure modes based on detected key features;PATENT Attorney Docket No. GROB-0028-WO project degradation patterns using overlaid inspection data; orestimate remaining life under different operational scenarios.

113. The method of claim 93, further comprising performing temporal analysis to perform at least one of:aligning current photogrammetric models with historical models;quantifying geometric changes over time;tracking degradation patterns; ordocumenting repair effectiveness.

114. The method of claim 93, wherein using the 3D model comprises providing interactive visualization capabilities that enable users interacting with a user interface to perform at least one of:navigating through the model in real-time;zooming into specific features or areas;accessing linked inspection data and / or reports; andextracting measurements directly from the model.

115. The method of claim 93, further comprising integration with operational systems to perform at least one of:automatically updating the 3D model following repairs;incorporating maintenance activity records;reflecting current asset configurations; andmaintaining accurate digital representation of physical asset condition.1 16. A method for field-based asset inspection and data management, the method comprising: compressing A-scan and B-scan data into optimized file formats for fast loading and rendering;rendering the compressed A-scan and B-scan;analyzing the data using a real-time processing module to provide instant feedback on asset condition;organizing and storing the data in a format that facilitates quick retrieval for further analysis or reporting; andproviding field operators with a user interface to manipulate and inspect the rendered data in real time.

117. The method of claim 116, wherein analyzing the data comprises implementing automated validation algorithms configured to:assess signal quality in real-time;identify potential measurement anomalies;PATENT Attorney Docket No. GROB-0028-WO verify data collection coverage; andalert operators to areas requiring additional inspection or validation.

118. The method of claim 116, wherein the optimized file formats include at least one of: indexed storage structures for rapid signal access;hierarchical data organization;compressed waveform data; orintegrated location information.

119. The method of claim 116, wherein providing the user interface comprises enabling field operators to:manipulate visualization parameters in real-time;compare current readings against historical data;annotate inspection findings; andinitiate additional data collection.

120. A system for asset focused non-destructive testing planning, the system comprising: an asset focused data structure comprising:an asset description;an asset identifier; andassociated data for the asset.

121. The system of claim 120, wherein the asset description includes geometric parameters comprising at least one of radius, height, outside diameter, wall thickness, or included angle values associated with defined components.

122. The system of claim 120, wherein the asset description includes nominal specifications comprising at least one of: wall nominal thickness, base thickness, corrosion allowance, minimum required thickness, coating nominal thickness, or material grade.

123. The system of claim 120, wherein the asset description includes organizational and contextual metadata comprising tags identifying at least one of: an organization, an entity, a site, a unit, a fleet identifier, a regulatory jurisdiction, or applicable standards.

124. The system of claim 120, wherein the asset description includes scene configuration and bounds data comprising: a compass orientation value, two-dimensional placement parameters, component bounds, or feature geometry ranges.

125. The system of claim 120, wherein the asset description includes lifecycle and operations metadata associated with the asset, comprising: installation date, effective date, maintenance and repair history references, sensor placement maps, calibration data, or validation artifacts.

126. The system of claim 120, wherein the asset comprises at least one of:PATENT Attorney Docket No. GROB-0028-WO an asset associated with an inspection operation;an example asset;a typical asset for a context, where the context comprises at least one of; a specific type of asset, an asset for a specific facility, an asset for a specific entity; ora prospective asset for inspection operation planning.

127. The system of claim 120, wherein the asset focused data structure is configured to register inspection and operational datasets to component- and feature-level geometry to enable asset-level and fleet-level analyses and to inform action selection comprising at least repair, replacement, maintenance, or enhanced inspection.

128. The system of claim 120, wherein a field-accessible interface exposes extensions that read from and write to the asset focused data structure using stable component and feature identifiers, effective dates, and tags to enable creation, modification, deployment, and validation of field tools without disrupting core platform integrity.

129. The system of claim 120, wherein a permissions subsystem applies a multidimensional matrix of role-, action-, and asset-specific controls at the component and feature granularity defined in the asset focused data structure, leveraging tags and stable identifiers to govern data visibility, tool usage, and configuration changes.

130. The system of claim 120, wherein a repair planning module identifies repair regions, executes repair simulations, and generates repair instructions by binding standards-based calculations and outcomes to specific features within the asset focused data structure and by versioning postrepair states via effective dates.

131. The system of claim 120, wherein a scenario simulation module generates inspection and operational scenarios, computes predicted outcomes on lifespan, performance, and failure risk, and compares scenarios while associating inputs and results to components and features defined in the asset focused data structure.

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