Methods and systems for monitoring and predicting component states

Abstract

A system includes a pump and sensors for monitoring one or more of: pressure, flow, pump speed, temperature, and / or other signals, and a processing system that executes one or more methods for identification of pump-associated system events, from the signals, corresponding to state changes and performance of the system and / or its subcomponents. Event identification is performed with neural network and / or other machine learning algorithms, with generation of novel training data sets. The sensor(s) can also be used to determine power consumption information about the system and / or its subcomponents. The system processes event-associated outputs for execution of actions for improving system performance, along with other downstream applications.

Description

FPAI-P07METHODS AND SYSTEMS FOR MONITORING AND PREDICTING COMPONENT STATESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application number 63 / 683,462 filed on 15-AUG-2024, which is incorporated in its entirety herein by this reference.

[0002] This application is also a continuation-in-part of U.S. Application number 19 / 040,440 filed on 29-JAN-2025, which is a continuation of U.S. Application number 18 / 542,008 filed on 15-DEC-2023 and now issued as U.S. Pat. No. 12,248,296 on 11-MAR- 2025, which is a continuation of U.S. Application number 18 / 295,342 filed on 04-APR-2023 and now issued as U.S. Pat. No 11,880,183 on 23-JAN-2024, which is a continuation of U.S. Application number 17 / 687,116 filed 04-MAR-2022 and now issued as U.S. Pat. No. 11,650,567 on 16-MAY-2023, which is a continuation of U.S. Application number 17 / 182,117 filed on 22-FEB-2021 and now issued as U.S. Pat. No. 11,300,942 on 12-APR-2022, which is a continuation of U.S. Application number 16 / 939,026 filed on 26-JUL-2020 and now issued as U.S. Pat. No. 10,962,955 on 30-MAR-2021, which claims the benefit of U.S. Provisional Application number 62 / 879,290 filed on 26-JUL-2019, which are each incorporated in its entirety herein by this reference.TECHNICAL FIELD

[0001] This invention relates generally to fields related to system monitoring and maintenance, and more specifically to new and useful methods and systems for predicting and monitoring component states, along with actions to prevent system failure.BACKGROUND

[0002] Replacement parts and repair labor contribute to a significant portion of the total maintenance costs of apparatuses (e.g., machines, vehicles, etc.). Reactive maintenance practices have significant and compounding negative effects that result in excess parts and labor expenses. In particular, issues that remain unaddressed, or that are addressed too late, typically lead to failures of multiple apparatus subcomponents, where such multi-FPAI-P07 component failures could have been prevented by addressing a failure or anticipated failure of the main subcomponent associated with the failure chain.

[0003] Furthermore, the technical domain knowledge required to properly troubleshoot and diagnose these complex systems is not readily available to the majority of equipment owners and operators that rely on these systems to produce their core products and services. Without the suitable tools and domain expertise, and when subject to delays caused by part sourcing and / or supply chain issues, failures can result in significant negative impacts. Unexpected downtime and poor system efficiency thus have associated costs that can be prevented or reduced with better monitoring, forecasting and troubleshooting systems. Current solutions for full system monitoring that can diagnose subcomponent issues in apparatus typically use a network of distributed sensors and custom algorithms. Implementing these options requires application-specific domain expertise, and the resources to design, deploy and maintain equipment is typically a non-optimal solution for apparatus owners and operators.

[0004] Thus, there is a need in fields related to system monitoring and maintenance, and more specifically, a need for new and useful methods and systems for improving apparatus reliability.BRIEF DESCRIPTION OF THE FIGURES

[0005] FIGURE 1A depicts an embodiment of a method for improving apparatus reliability.

[0006] FIGURE 1B depicts an embodiment of a method for improving apparatus reliability across a group of apparatuses.

[0007] FIGURE 2 depicts an embodiment of a system configured to mount to a pump of an apparatus being monitored according to methods described.

[0008] FIGURE 3A depicts exemplary model architecture used to generate apparatus subcomponent statuses.

[0009] FIGURES 3B and 3C depict expanded aspects of model architecture for monitoring apparatuses.

[0010] FIGURE 4 depicts an exemplary workflow for providing reliability as a service.FPAI-P07

[0011] FIGURES 5A-5B depict exemplary user interfaces for providing insights into subcomponent statuses, according to a method and system for providing reliability as a service.

[0012] FIGURES 6A-6B depict variations and examples of system components and method steps for executing action related to monitoring and remote addressing of apparatus faults.

[0013] FIGURE 7 depicts an embodiment of a system for monitoring and remote addressing of apparatus faults.

[0014] FIGURE 8 depicts a computer system configured to improving apparatus reliability.DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The following description of the preferred embodiments of the invention is not intended to limit the invention to these preferred embodiments, but rather to enable any person skilled in the art to make and use this invention.1. Benefits

[0016] The inventions associated with the system and method can confer several benefits over conventional systems and methods, and such inventions are further implemented into many practical applications across various disciplines.

[0017] In embodiments, the invention(s) provide a smart pump system including an advanced hydraulic pump that integrates innovative edge-embedded artificial intelligence hardware components, described herein. This revolutionary system is designed to monitor and predict the wear and potential failure of critical consumable components, such as seals, bearings, valves, and other components in real-time, and with edge-deployed functionality.

[0018] With respect to edge-deployed, updatable, and / or self-training artificial intelligence (Al) monitoring, systems described (e.g., smart pump system) are equipped with an Al kernel that continuously monitors operation of the pump. The Al kernel is structured to analyze data from vibration, temperature, and pressure (and / or other signal types) to predict when components are nearing the end of their service life or are at risk of imminent failure.FPAI-P07

[0019] With respect to predictive maintenance, by forecasting potential failures, systems described (e.g., smart pump system) ensure that maintenance is performed at the optimal time, thereby reducing unplanned downtime and extending the overall life of the pump. This predictive capability allows for the early identification of issues, enabling proactive maintenance before they escalate into costly repairs or catastrophic failures.

[0020] With respect to automated service integration: systems described (e.g., smart pump system) seamlessly connect to service shops via a cloud-based platform, thereby providing reliability as a service (RAAS). When a component's wear and usage indicate optimal service, embodiments of the system automatically notifiy the connected service shop and schedules the required maintenance. The necessary replacement parts or a new pump are pre-ordered and shipped directly to the service provider, ensuring they have all the components on hand when the pump arrives for servicing.

[0021] With respect to efficiency for service shops, service providers benefit from this integration by receiving detailed work orders in advance. As such, ervice providers know precisely what components need replacement and can plan their inventory and workload accordingly, reducing wait times and improving service efficiency. The system embodiments’ detailed diagnostics also ensure that technicians are fully informed about the condition of the pump before it arrives, streamlining the repair process.

[0022] With respect to sales and inventory management, for manufacturers and distributors, systems described (e.g., smart pump system) provide an automated channel for selling replacement parts and new pumps. Given that the Al kernel and other aspects predict the need for a replacement, system analytics are configured to trigger an order directly, ensuring that the right parts are always available in the right quantities, minimizing inventory costs, and maximizing sales opportunities.

[0023] Additional benefits include:

[0024] Reduced Downtime: By predicting failures before they occur, the systems described (e.g., smart pump system) and methods described minimize unplanned downtime, keeping operations running smoothly.FPAI-P07

[0025] Optimized Maintenance: Maintenance schedules are optimized based on actual component wear, rather than arbitrary time intervals, resulting in cost savings and increased pump lifespan.

[0026] Improved Service Efficiency: Service shops receive all necessary information and parts in advance, making their work more efficient and reducing turnaround times.

[0027] Increased Sales for Service Providers: Automated parts ordering ensures that service providers can easily sell and replace consumables and pumps as needed, enhancing their service offerings and revenue streams.

[0028] The systems described (e.g., smart pump system) and methods described represents the future of hydraulic systems, where intelligence and connectivity lead to smarter, more efficient, and more profitable maintenance practices.

[0029] In embodiments, the inventions can additionally provide reliability as a service(RAAS), by optimizing reductions in apparatus maintenance. Exemplary apparatus types to which the inventions can be applied can include one or more of: hydraulic apparatuses, vehicles (e.g., terrestrial vehicles, aerial vehicles, space craft, watercraft, etc.), power systems, apparatuses with vibrating components, electrical systems, energy infrastructure, other utility infrastructure, battery systems, and / or other apparatus types.

[0030] In embodiments, the inventions can optimize reductions in the cost of apparatus maintenance, as enabled by on-board (e.g., edge-deployed) processing that is structured to deliver actionable insights complete with root cause prognostics that can ultimately eliminate existing labor costs associated with troubleshooting. Rapid on-site troubleshooting, in combination with recommendations for addressing issues prior to catastrophic failure, significantly lowers replacement part expenses. As such, the inventions can enable rapid deployment of diagnostics and solutions at the edge (e.g., with edge- deployed computing and sensors), without requiring remote processing of data and nonrapid solutions.

[0031] Exemplary outcomes demonstrated by the inventions involved the use of predictive maintenance, enabled by real-time actionable insights, to reduce the annual cost of hydraulic parts and labor by 34% and increase the available maintenance labor force by 7%, in a representative fleet. If this outcome was projected to a situation involvingFPAI-P07 maintenance of a fleet with 100 trucks, there would be over $56oK in annual savings and release of up to 3,500 hours of technician time, in comparison with current approaches for hydraulic vehicle fleet maintenance.

[0032] In embodiments, the invention(s) can reduce apparatus maintenance costs by at least 10%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, or greater, in comparison with existing maintenance costs. In relation to hydraulic vehicle maintenance costs, the invention(s) can anticipate or detect issues with chassis components, hydraulic subcomponents, brake subcomponents, tires, wheels, and / or engine subcomponents, and provide solutions for repairing or replacing such subcomponents efficiently.

[0033] In embodiments, the invention(s) can enable identification of apparatus subcomponents that are past serviceable lives, and that are candidates for replacement. The invention(s) can enable identification of apparatus subcomponents that are within serviceable lives, and provide solutions for efficiently servicing such subcomponents. In embodiments, the invention(s) can include generating diagnostics for serviceable subcomponents, and, if the operator would rather replace certain subcomponents, the invention(s) can involve management of a marketplace for refurbishing serviceable subcomponents that have been relinquished. In one example described here, an apparatus can include a hydraulic refuse truck. A component of the hydraulic refuse truck can include a chassis component, a hydraulic component, a brake component, a tire component, a pump component, an engine component. A subcomponent can include a valve subcomponent, a cylinder subcomponent, or another suitable subcomponent. Exemplary apparatuses, components, and subcomponents across various fields of use are described in more detail below.

[0034] In embodiments, the invention(s) can include providing replacement subcomponents (e.g., unlimited replacement subcomponents) to an owner, operator, or maintainer of a group of apparatuses (e.g., at a low fixed cost, according to a subscription model, etc.). In an example, a service plan can provide unlimited, optimally timed replacement components for the group of apparatuses, based upon real-time apparatus monitoring as described. Returned subcomponent statuses notify a platform (e.g., centralFPAI-P07 platform, decentralized platform) when subcomponents are near their efficiency threshold, and automatically delivers replacement subcomponents. The subcomponents that are near their efficiency threshold can then be retrieved for benchmarking and overhaul. Overhauled components can then be stored in inventory (e.g., as in a subcomponent marketplace), and in the future, provided for replacement of other subcomponents that are near their efficiency thresholds. Benefits of the plan include: lower hydraulic parts cost, predictable budgeting, optimization of apparatus performance by providing real-time monitoring and pre-emptive servicing, elimination of troubleshooting labor costs, and improvements to safety and environmental issues (e.g., by ensuring safe operating parameters, elimination of oil spills, elimination of environmental impacts due to proactive maintenance, etc.).

[0035] In embodiments, the inventions(s) can be used to provide fleet-wide assessments, such that service solutions account for the fleet as a whole, with the goal of keeping the entire fleet operating as optimally as possible. For example, in one embodiment, the invention(s) can guide harvesting of subcomponents of one vehicle of the fleet to ensure proper operation of the remainder of the fleet, where removing the harvested vehicle from service produces a "greater good" solution that maintains the highest level of performance of the fleet.

[0036] In embodiments, the inventions(s) can be used to provide assessments across multiple units of apparatuses, such that service solutions account for a grouping of units, with the goal of keeping the entire group operating as optimally as possible. For example, in one embodiment, the invention(s) can guide harvesting of subcomponents of one unit to ensure proper operation of the remainder of the group of units, where removing the harvested unit from service produces a "greater good" solution that maintains the highest level of performance of the group of units.

[0037] In embodiments, the invention(s) omit requirements to disassemble apparatuses being diagnosed, thereby saving time and costs associated with disassembly and reassembly. Exemplary system components that enable diagnostics to be performed without disassembly and reassembly are described below.

[0038] Additionally or alternatively, the invention(s) can confer any other suitable benefit.FPAI-P072. Method

[0039] As shown in FIGURE 1A, an embodiment of a method too includes: For an individual apparatus, providing a mounting interface between a sensor subsystem and the apparatus, thereby coupling the sensor subsystem to the apparatus at a single position Sno; sampling a set of signal streams from the sensor subsystem (e.g., during operation of the apparatus) S120; returning a set of statuses of a set of subcomponents of the apparatus, upon applying a set of transformations to the set of signal streams S130, without requiring any disassembly of the apparatus; returning a recommended action for increasing or optimizing reliability of the apparatus based upon a diagnosed status of at least one of the set of subcomponents S140; and optionally, executing the recommended action S150.

[0040] As shown in FIGURE 1A, an embodiment of a method 100 includes: For an individual apparatus, providing a mounting interface between a sensor subsystem and the apparatus, thereby coupling the sensor subsystem to the apparatus at a single position S110; sampling a set of signal streams from the sensor subsystem (e.g., during operation of the apparatus) S120; returning a set of statuses of a set of subcomponents of the apparatus, upon applying a set of transformations to the set of signal streams S130, without requiring any disassembly of the apparatus; returning a recommended action for increasing or optimizing reliability of the apparatus based upon a diagnosed status of at least one of the set of subcomponents S140; and optionally, executing the recommended action S150.

[0041] As shown in FIGURE 1B, an embodiment of a method 100 includes: coupling an advanced hydraulic pump comprising a sensor subsystem, to an apparatus, S210; sampling a set of signal streams from the sensor subsystem (e.g., during operation of the apparatus) S220; returning a set of statuses of a set of subcomponents of the apparatus, upon applying a set of transformations to the set of signal streams S230, without requiring any disassembly of the apparatus; returning a recommended action for increasing or optimizing reliability of the apparatus based upon a diagnosed status of at least one of the set of subcomponents S240; and optionally, executing the recommended action S250.

[0042] Relatedly, as shown in FIGURE 1C, an embodiment of a method 300 for providing reliability as a service includes: For a set of apparatuses, providing mounting interfaces between units of a sensor subsystem and corresponding apparatuses of the set ofFPAI-P07 apparatuses at a single position for each of the set of apparatuses S310; sampling a set of signal streams from each unit of the sensor subsystem (e.g., during operation of the apparatuses) S320; returning a set of statuses of the set of apparatuses with global and subcomponent resolution, upon applying a set of transformations to the set of signal streams S330, without requiring any disassembly of each of the set of apparatuses; returning a recommended action for increasing or optimizing reliability of the set of apparatuses, based upon the set of statuses S340; and optionally, executing the recommended action S350.

[0043] The methods 100, 200 function to provide a minimally-invasive approach or non-invasive approach (e.g., using non-contact sensors) to diagnosing apparatuses individually and / or in groups, and to recommend or execute solutions for treating conditions of the apparatuses. The methods 100, 200 can be implemented using embodiments, variations, and examples of system components described in one or more of: U.S. Application No. 16 / 939,026 filed on 26-JUL-2020 and now issued as U.S. Pat. No. 10,962,955 on 30-MAR-2021; U.S. Application No. 18 / 333,037 filed on 12-JUN-2023; U.S. Application No. 18 / 342,525 filed on 27-JUN-2023; and U.S. Application No. 18 / 497,913 filed on 27-JUN-2023, which are each herein incorporated in its entirety by this reference.2.1 Application Areas

[0044] In specific examples, the method(s) 100, 200 described can be applied to apparatuses in various industries, in order to provide reliability as a service.

[0045] For instance, the method(s) 100, 200 described can be applied to hydraulic equipment (e.g., heavy mobile equipment, such as excavators, oil rig drilling apparatuses, cement trucks, garbage trucks, etc. and / or fixed equipment, such as injection molding machines, overhead cranes, etc.), where maintaining reliability can be based upon monitoring and remediating subcomponents including one or more of: one or more pump subcomponents, a power takeoff (PTO) subcomponent, a valve bank-arm subcomponent, a valve-cartridge subcomponent, a cylinder-arm extend subcomponent, a cylinder-clamp subcomponent, a motor-cart dump subcomponent, a valve bank-body subcomponent, a cylinder-hopper cover subcomponent, a cylinder-dump subcomponent, a cylinder-packer subcomponent, a cylinder-door subcomponent, or another suitable subcomponent, as shown in FIGURE 2. Exemplary components can include one or more of: bushings, gears,FPAI-P07 seals, rods, other pump components, other cylinder components, etc. In a specific example, the annual hydraulic maintenance cost per hydraulic truck of a fleet can be up to $3?K, and implementation of the method(s) described can reduce this cost by percentages indicated above. According to steps S140 and S240 described above, such subcomponent statuses can be monitored without requiring disassembly of the apparatuses, and issues with such subcomponents can be addressed in an efficient manner that does not lead to a cascade of failed subcomponents that would otherwise contribute to unnecessarily high repair and labor resource requirements. Furthermore, reliability optimization can be performed at the fleet level, where solutions may involve reducing reliability (e.g., temporarily reducing reliability) of one or more units of the fleet, in order to globally enhance reliability of performance of the entire fleet.

[0046] Additionally or alternatively, the method(s) 100, 200 described can be applied to any equipment with a vibrating component. Exemplary equipment include apparatuses with motor components (e.g., shaft components, fan components, rotor components, bearing components, sheave components, cylinder components, piston components, etc.). Subcomponents can also include associated components, such as contaminants, liquids (e.g., oils, etc.), and / or other components. Diagnostics of such apparatuses can include subcomponent evaluations related to a set of harmonic faults of the apparatus, a set of synchronous faults of the apparatus, a set of sub-harmonic and sub-synchronous faults of the apparatus, and a set of non-synchronous faults of the apparatus, where embodiments, variations, and examples of fault statuses are described in U.S. Application No. 18 / 333,037 filed on 12-JUN-2023.

[0047] Additionally or alternatively, the method(s) 100, 200 described can be applied to any electrical equipment, where electrical equipment subcomponents can include one or more of: electric motors, electric actuators, rotors, stators, bearings, complete printed circuit board assemblies (PCBAs)and systems of PCBAs, fuses, passive components (e.g., capacitors, resistors, diodes, etc.), and active components (integrated chips, etc.). Diagnostics of such apparatuses can include subcomponent evaluations related to short circuit faults, over and under-discharge faults, connector faults, insulation faults, and thermal management faults, while failure modes of the electric motor can include bearingFPAI-P07 faults, stator faults, and rotator faults, in order to provide improved reliability based upon treatment of such fault situations.

[0048] Additionally or alternatively, the method(s) 100, 200 described can be applied to power systems for storing and / or distributing energy. In specific examples, the invention(s) can be applied to monitoring of systems for storing and distributing power, in the context of solar energy systems, solar-thermal energy systems, wind energy systems (e.g., on-shore wind energy systems, off-shore wind energy systems), geothermal energy systems, hydropower energy systems, ocean energy systems, tidal energy systems, biomass energy systems, non-renewable energy source systems, and other power systems.

[0049] In the context of solar energy systems, the method(s) 100, 200 described can be applied to subcomponents including one or more of: solar panel components, inverter components, energy storage components, electrical panel components, electric meter components, interfaces to grid components, grid components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0050] In the context of solar-thermal energy systems, the method(s) 100, 200 described can be applied to subcomponents including one or more of: solar panel components, inverter components, electrical panel components, electric meter components, mirror components, receiver components, heat exchanger components, storage tank components, interfaces to grid components, grid components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0051] In the context of wind energy systems (e.g., on-shore wind energy systems, offshore wind energy systems), the method(s) 100, 200 described can be applied to subcomponents including one or more of: rotor components, nacelle components, tower components, gearbox components, generator components, inverters, foundation components, inter-array cable components, substation components, export cable to onshore interconnection components, interfaces to grid components, grid components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.FPAI-P07

[0052] In the context of geothermal energy systems, the method(s) 100, 200 described can be applied to subcomponents including one or more of: heat exchanger components, system pump components, valve components, compressor components, turbine components, generator components, cooling tower components, interfaces to grid components, grid components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0053] In the context of hydropower energy systems, the method(s) 100, 200 described can be applied to subcomponents including one or more of: generator components, transformer components, powerhouse components, turbine components, components associated with intakes from a reservoir, components associated with control gates, components associated with penstock access, transformer components, interfaces to grid components, grid components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0054] In the context of ocean energy and / or tidal systems, the method(s) 100, 200 described can be applied to subcomponents including one or more of: steam condenser components, liquid pump components, vacuum pump components, heat exchanger components, turbine components, turbine tunnel components, sluice gate components, ram joint components, turbine components, interfaces to grid components, grid components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0055] In the context of biomass energy systems, the method(s) 100, 200 described can be applied to subcomponents including one or more of: fuel system components, steam production system components, turbine components, generator components, transformer components, interfaces to grid components, grid components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0056] Other components can include power plant components, transformer components for stepping up voltage, transmission line components, transformerFPAI-P07 components for stepping down voltage, distribution line components, and / or other components.

[0057] In the context of electric vehicles, the method(s) 100, 200 described can be applied to subcomponents including one or more of: energy management system components, battery components, inverter components, electric motor components, drivetrain components, regenerative braking system components, other electric vehicle electrical system components, and / or other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0058] In the context of electric vehicle chargers, the method(s) 100, 200 described can be applied to subcomponents including one or more of: alternating current (AC) supply components (e.g., single phase, 3-phase, fixed supply, etc.), metering and billing components, safety interlock components, components of level 1 and level 2 chargers (e.g., rectifier components, power control unit components, direct current (DC) converter components, protection components, battery monitor components, battery management components, etc.), components of level 3 chargers (e.g., control area network (CAN) bus control and authentication components, protection circuit components, battery monitor components, battery management components, etc.), inverter components (e.g., AC / DC bidirectional inverter components, other vehicle-to-grid charging components, and other components in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents.

[0059] In the context of batteries and / or battery management systems, the method(s)100, 200 described can be applied to subcomponents including cathode components, anode components, separator components, cells, architecture for coupling batteries in parallel, architecture for coupling batteries in series, electrodes, housings, thermal management / cooling systems, electrolytes, separators, containers / housings, connectors, terminals, protective circuitry, devices incorporating such batteries, and / or other subcomponents in order to provide improved reliability based upon preventative and / or proactive maintenance or replacement of such subcomponents. In a specific use case, the methods 100, 200 can be applied to vehicle batteries (e.g., electric vehicle batteries, hybridFPAI-P07 vehicle batteries, non-electric vehicle batteries, etc.), where the batteries can be configured to maintain themselves and / or sell themselves as needed, based upon diagnostics generated according to methods described, and seamless marketplace integration for replacing or repairing subcomponents based upon actual and / or anticipated failures of subcomponents.

[0060] With respect to battery manufacture, the method(s) too, 200 described can be applied monitoring of subcomponents and / or assemblies during stages of manufacturing, including one or more of: raw material preparation; electrode preparation, electrode drying and calendaring; electrolyte preparation; separator coating (e.g., for some battery types); cell assembly; electrolyte filling; sealing; formation and aging; quality testing; module and pack assembly; final testing and inspection; and / or other stages.2.2 Method - Sensor Interfaces

[0061] For an individual apparatus, Step S110 recites: providing a mounting interface between a sensor subsystem and the apparatus, thereby coupling the sensor subsystem to the apparatus at a single position; relatedly, Step S210 recites: for a set of apparatuses, providing mounting interfaces between units of a sensor subsystem and corresponding apparatuses of the set of apparatuses at a single position for each of the set of apparatuses. Steps S110 and S210 function to interface sensors (e.g., compact sensors) with apparatuses at single or a small number of relevant positions for detecting signals from the apparatuses, in order to enable on-site diagnostics to be performed, without disassembly of such apparatuses.

[0062] Embodiments, variations, and examples of such sensors (e.g., contact sensors, non-contact sensors, etc.), mounting interfaces, and mounting positions are described in one or more of: U.S. Application No. 16 / 939,026 filed on 26-JUL-2020 and now issued as U.S. Pat. No. 10,962,955 on 30-MAR-2021; U.S. Application No. 18 / 333,037 filed on 12- JUN-2023; U.S. Application No. 18 / 342,525 filed on 27-JUN-2023; and U.S. Application No. 18 / 497,913 filed on 27-JUN-2023, which are each incorporated in its entirety by reference above.

[0063] Exemplary sensors can include one or more of: flow sensors, pump demand sensors, temperature sensors, pressure sensors, voltage sensors, current sensors, vibration sensors, and auxiliary sensors.FPAI-P072.3 Method - Signal Sampling

[0064] Step S120 recites: sampling a set of signal streams from the sensor subsystem (e.g., during operation of the apparatus); relatedly, Step S220 recites: sampling a set of signal streams from each unit of the sensor subsystem (e.g., during operation of the apparatuses). Steps S120 and S220 function to monitor a discrete (e.g., minimized) set of signal types and / or number of parameters, from which performance of the apparatuses and / or demand on the apparatus can be extracted. The data derived from the signals can then be processed according to methods described in more detail below, in order to efficiently assess statuses of and / or anticipate events of the apparatus and its subcomponents. Signal resolution and other parameters are described in one or more of: U.S. Application No. 16 / 939,026 filed on 26-JUL-2020 and now issued as U.S. Pat. No. 10,962,955 on 30-MAR-2021; U.S. Application No. 18 / 333,037 filed on 12-JUN-2023; U.S. Application No. 18 / 342,525 filed on 27-JUN-2023; and U.S. Application No. 18 / 497,913 filed on 27-JUN-2023, which are each incorporated in its entirety by reference above.2.4 Method - Signature Extraction and Subcomponent Status Return

[0065] Step S130 recites returning a set of statuses of a set of subcomponents of the apparatus, upon applying a set of transformations to the set of signal streams, without requiring any disassembly of the apparatus; relatedly, Step S230 recites: returning a set of statuses of the set of apparatuses with global and subcomponent resolution, upon applying a set of transformations to the set of signal streams. Steps S130 and S230 function to receive, as inputs, the data streams of Steps S120 and S220, respectively, in order enable extraction of signatures corresponding to events (e.g., historical events, anticipated events, usage, etc.) and / or statuses (e.g., health statuses) of the apparatus (e.g., at global and subcomponent levels), in relation to various faults associated with subcomponents and combinations of subcomponents described.

[0066] Statuses of subcomponents can then be used to guide interventions and / or treatments for repair or replacement of subcomponents, in order to break a cascade of catastrophic failures, according to subsequent steps of the method.FPAI-P07

[0067] Exemplary statuses can include one or more subcomponent fault states describedin Applications incorporated by reference above. In examples, the set of signatures comprises a signature indicative of a cavitation event involving fluid of the apparatus; the set of signatures comprises a signature indicative of a wear event and a cause of the wear event of a subcomponent of the apparatus; the set of signatures comprises a signature indicative of a leakage event and a cause of the leakage event of a subcomponent of the apparatus; and other suitable signatures corresponding to subcomponent events.

[0068] Exemplary statuses can further include one more of: estimated lives of subcomponents, estimated lives of the apparatus (e.g., with and without replacement of subcomponents with indicated fault states), subcomponents that are anticipated to fail in a cascade of subcomponent failures, subcomponents that are anticipated to fail next if a subcomponent with a fault state is not addressed, estimates of replacement costs for a subcomponent, estimates of repair costs for a subcomponent, estimates of time to replace a subcomponent, estimates of time to repair a subcomponent, and / or other suitable subcomponent statuses.

[0069] In examples, life estimates can be generated from conversion models of fault statuses and severities of faults. Life estimates can also be returned from models trained to process input fault states or combinations of fault states of different subcomponents, and to return life estimates. Life estimates can be provided in terms of seconds, minutes, hours, days, months, years, or at any time scale.

[0070] In relation to performing transformation operations to derive apparatus statuses, embodiments of processing subsystem components described (e.g., in applications incorporated by reference) can receive and process signal streams at the edge, in relation to edge-deployed devices described. Receiving and processing signals can additionally or alternatively be achieved with use of a wireless or wired connection, using any suitable transmission protocol. Receiving the set of data streams and performing the set of transformation operations can be performed real-time (e.g., with information transfer without significant delay from the time of initial signal generation, thereby enabling rapid responses). Additionally or alternatively, receiving the set of data streams and performingFPAI-P07 the set of transformation operations can be performed non-real time (e.g., with postprocessing delay).

[0071] In a specific example, Steps S130 and S230 can involve or be executed by way of an embedded controller comprising a computer comprising circuitry for interfacing with sensors and / or mounting interfaces of , with peripherals for debugging and wireless communication. In the specific example, the computer is an embedded single board computer of the signal conditioning and communications subsystem 130 that implements a Xilinx K26 Kria™ system-on-module (SOM) structured for edge computer vision applications, with artificial intelligence (Al) performance attributed to Zynq MPSoC architecture and architecture configured with various deep learning processing unit (DPU) or neural processing unit (NPU) configurations. Various DPU / NPU configurations (e.g., at 300Hz) of the signal conditioning and communications subsystem 130 can provide performance of the following, in trillions of operations per second (TOPS): 0.5 TOPS, 0.6 TOPS, 0.7 TOPS, 0.8 TOPS, 0.9 TOPS, 1 TOPS, 1.1 TOPS, 1.2 TOPS, 1.3 TOPS, 1.4 TOPS, 1.5 TOPS, 2 TOPS, 3 TOPS, 4 TOPS, or greater, thereby achieving unprecedented performance with respect to computing operations involving machine learning architecture for such novel sensor configurations for monitoring apparatus.

[0072] The signal conditioning and communications subsystem aspects described in the example above (e.g., Kria K26 SOM) supports a full range of data type precisions such as FP32, INT8, binary, and other custom data types, and operations on lower precision data type can consume low power (e.g., less than 0.03 picojoules, less than 0.05 picojoules, less than 1 picojoule, less than 10 picojoules, less than 50 picojoules, or up to 700 picojoules, depending upon operation).

[0073] In a specific example, processing subsystem components that extract apparatus statuses from sensor signal streams include an NPU with 1 trillions of operations per second (TOPS) capability with energy use performance of less than 1 picojoule per operation. The NPU can include self-attention time-series transformer architecture comprising an encoder block comprising multi-head attention subarchitecture, described in more detail below. The self-attention time-series transformer architecture of the NPU can omit a decoder block, as described in more detail below. The NPU can be on-chip, for edgeFPAI-P07 deployment applications described. Variations of the processing unit can alternatively include other architecture, as described.2.4.1 Method - Apparatus Status Extraction - Self-Attention Architecture

[0074] As introduced above, exemplary model architecture(s) used to return a set of statuses of a single apparatus or group of apparatuses include the following:

[0075] Self-attention time-series transformer architecture with masking: Such architecture applies a modified self-attention transformer-based architecture capable of processing multivariate time series data, in order to encode sensor data of the sensors described above, in an unsupervised fashion. In doing so, the measured signal(s) of the apparatus and / or subcomponents described above are embedded into a latent space representation of the temporal dynamics of the system. The latent space embedding is then used to classify failure modes and other health statuses of each apparatus involved.

[0076] In examples, model architecture includes a deep neural network to encode the physics of the apparatus(es) by monitoring signals associated with output(s) described, as well as time-series signals of relevant system characteristics such as operational state of the apparatuses (e.g., transportation operational modes, demand operational modes, etc.). The model can thus be used to monitor the overall health of the apparatus(es) at global and subcomponent levels, and predict failure modes of systems subcomponents described.

[0077] In more detail, the model applies a modified self-attention transformer based architecture capable of processing multivariate time series data to encode the discrete types of data in an unsupervised fashion. Variations of the architecture can alternatively encode a subset of signal types (e.g., with encoding of reduced subsets of signal types in relation to signal types described, etc.). The modified transformer encoder takes as input training samples, X E I?wxm, which are multivariate time series of length w and m different variables. The training samples (e.g., training dataset) can be acquired from the sensor subsystem described, where the training samples include data streams associated with acquired signals / data streams (e.g., voltage signal streams, current signal streams, temperature signal streams, resistance signal streams, impedance signal streams, and / or auxiliary signal streams, etc.), with labels corresponding to known faults, failure modes, and statuses of the apparatus at global and subcomponent levels. To generate the training dataset(s),FPAI-P07 apparatuses and subcomponents with undiagnosed / unlabeled faults, failure modes, and statuses can be operated, and sensor signals can be acquired to generate the training dataset, with subsequent verification of classified statuses, with unsupervised multistage training.

[0078] The original feature vectors xtare first normalized by subtracting the mean and divide by the variance across the samples of the training dataset. The normalized inputs are then linearly projected with bias onto a d-dimensional vector space, where d is the dimension of the transformer model sequence element representations: ut= Wpxt+ bp, where lVpG Rdxmand bpG Rdare fully learnable parameters and utG Rd, t = 0, .. ,w are inputs to the transformer encoder. These inputs, which were transformed from the training dataset, become the queries, keys and values feeding into the self-attention layer, after adding the positional encodings corresponding to time, tw. The multi -headed attention mechanism is altered by changing the normalizations from layer based to batch based, which allows for better handling of outlier signal data, and utilizing a Gaussian error linear unit (GELU) instead of a rectified linear unit (ReLU).

[0079] In a standard transformer which employs an encoder-decoder framework, the result of the transformer encoder block is sent along with the target shifted output sequence to the decoder block in a supervised fashion.

[0080] Embodiments of the model architecture can implement an alternative approach, with inclusion of architecture for seeking unbiased embedding of the health state of the apparatus(es) involved. The decoder block can be omitted, and instead a single linear layer is used to predict the normalized input values. The degree to which these predicted system values recapitulate the source data allows the model to learn interdependencies between the monitored variables of the system, embed these relationships into a high dimensional state, and predict the state of the apparatus or subcomponents from sparse input data, in a manner that produces a higher level of efficiency with respect to computations performance and energy usage.

[0081] The multivariate time-series encoder can be trained in two stages, as indicated above. First, an unsupervised pre-training is performed to autoregress unlabeled time-series data of the training dataset. The goal of the unsupervised training is to encode an input spectrum into a meaningful latent space. The resulting embedding allows both the originalFPAI-P07 spectra to be decoded directly from the latent space representation but also facilitates downstream application for classification and forecasting of failure modes in associated apparatuses. As described above, application-of-use-specific auxiliary sensor data can be processed by the model with unassisted auto regression to embed associated signatures and fine tune application-of-use-specific models.

[0003] FIGURE 3A depicts a schematic of exemplary neural network architecture for the modified attention-based transformer encoder used to autoregress multivariate time series data of one or more signal types described with respect to apparatus subcomponents described. In more detail with respect to model architecture, input data 70 is processed with a custom masking 71, followed by processing with a positional encoding linear layer 72 and an input embedding linear layer 73. Outputs of the positional encoding linear layer 72 and the input embedding linear layer 73 are then processed with an encoder block 80 including multi-head attention subarchitecture 81, followed by first batch normalization architecture 82, a first linear layer 83, a Gaussian error linear unit 84, a second linear layer 85, and second batch normalization architecture 86. Outputs of the encoder block include signature(s) 87, which are returned to a third linear layer 90 to generate second input data 91 (e.g., where autoregression architecture of the model is structured such that input data 91 is attempting to match input data 70, with iterative training).

[0004] In variations of model architecture, as shown in FIGURES 3B and 3C, signature 87 can then used to predict health and / or various states of the battery 10, using either a single temporal neural network (as shown in FIGURE 3B). or an ensemble of temporal neural networks (as shown in FIGURE 3C). However, other variations of model architecture can also be used. An exemplary regressor model for predicting battery health can include Temporal Convolutional Network (TCN) architecture (as shown in FIGURE 3B). In more detail with respect to the exemplary TCN architecture, the model is structured to receive raw input data (70) and a signature from the autoencoder (87) as initial inputs. Signature 87 is processed through one or more TCN Block(s) 88 with varying timescales and depth, which provide a core temporal feature extraction mechanism. Within each TCN Block 88, the input is processed along two parallel paths defined by the model architecture: a first path provides a main convolutional path 88a and a second path 88b provides a residualFPAI-P07 connection path. The main convolutional path 88a includes a weight normalized one dimensional (1-D) convolution layer 89, followed by a Rectified Linear Unit activation 103, followed by another i-D convolution layer 104, and a subsequent Rectified Linear Unit activation 105. Optionally, a dropout can be added before the activations (ReLU 103, ReLU 105). Concurrently (e.g., in parallel), a residual connection path 88b downsamples the input signature 87 and processes it through a third 1-D convolutional layer 102 to adjust dimensions for summation. The outputs of these two paths are combined, forming a residual connection that enhances gradient flow and model stability. The output of the TCN Blocks(s) 88 are then pooled 106, which aggregates temporal features. In parallel, the raw input data 70 is used to compute additional relevant features 107 orthogonal to the temporal patterns captured by the TCN 88, using covariate processing architecture. The processed features from the adaptive pool 106 and the covariate processing architecture 107 are then combined and passed through a linear layer 108, an activation rectified linear unit 109, and a second linear layer (111). The final output of the last linear layer 111 represents the predicted battery health 112 and / or other component status aspects. The predicted battery health 112 from the exemplary TCN regressor (as shown in FIGURE 3A), may either by used directly or combined with the predictions from other regressors (as shown in FIGURE 3B; regressor 113), such as other transformers, recurrent neural networks, multilayer perceptrons, or other TCNs with varying parameters, to form an ensemble prediction of battery health (As shown in FIGURE 3B, with ensemble health prediction 114).

[0082] The exemplary neural network architecture can include Pytorch implementation of a transformer encoder for multivariate time series containing 6 variables with a model dimension of size 128, 8 attention heads, a forward expansion of 4x in the feedforward block, and a dropout fraction of 0.1 after batch normalization.

[0083] In relation to subsystems and equipment described, exemplary failure modes and other statuses that are returned by trained model architecture relate to anticipation of a set of faults including one or more faults described in applications incorporated by reference.

[0084] Variations of model architecture are further described in one or more of: U.S. Application No. 16 / 939,026 filed on 26-JUL-2020 and now issued as U.S. Pat. No. 10,962,955 on 30-MAR-2021; U.S. Application No. 18 / 333,037 filed on 12-JUN-2023; U.S.FPAI-P07Application No. 18 / 342,525 filed on 27-JUN-2023; and U.S. Application No. 18 / 497,913 filed on 27-JUN-2023, which are each incorporated in its entirety by reference above.2.5 Method - Reliability Recommendations, Therapeutic Interventions, andOptimization for Apparatuses

[0085] Step S140 recites: returning a recommended action for increasing or optimizing reliability of the apparatus based upon a diagnosed status of at least one of the set of subcomponents S140, and step S150 recites: optionally, executing the recommended action.

[0086] Steps S140 and S150 function to utilize returned statuses (e.g., anticipated or actual fault states) of subcomponents from step S130 to guide and perform actions for improving reliability of the apparatus, as a service. In particular, statuses can describe one or more subcomponent fault states, estimated lives of subcomponents, estimated lives of the apparatus (e.g., with and without replacement of subcomponents with indicated fault states), subcomponents that are anticipated to fail in a cascade of subcomponent failures, subcomponents that are anticipated to fail next if a subcomponent with a fault state is not addressed, estimates of replacement costs for a subcomponent, estimates of repair costs for a subcomponent, estimates of time to replace a subcomponent, estimates of time to repair a subcomponent, and / or other suitable subcomponent statuses.

[0087] With identification of subcomponents that are near the ends of their serviceable lives and / or are past serviceable lives (e.g., based upon life estimates described above), recommended and executed actions can include automatically initiating procedures for providing a replacement subcomponent. Providing a replacement subcomponent can be performed with or without the operator or maintainer's input. Providing a replacement subcomponent can be performed with the operator or maintainer's authorization. In an example, Steps S140 and S150 can include generating an edge-deployed set of subcomponent statuses without disassembling the apparatus, and based upon a fault state of a subcomponent status, generating instructions for ordering and delivering a replacement subcomponent.FPAI-P07

[0088] In an example, returning a recommended action and executing the recommended action can include providing instructions (e.g., via a suitable user interface), for accessing and replacing or repairing a subcomponent with a fault status.

[0089] In an example, returning a recommended action and executing the recommended action can include providing control instructions to a robotic device that has functionality for accessing, disassembling, and / or repairing a subcomponent with a fault status.

[0090] In an example, returning a recommended action and executing the recommended action can include providing replacement subcomponents (e.g., unlimited replacement subcomponents) to an apparatus owner, operator, or maintainer (e.g., at a low fixed cost, according to a subscription model, etc.). In an example, a service plan can provide unlimited, optimally timed replacement components for the apparatus, based upon realtime apparatus monitoring as described. Returned subcomponent statuses notify a platform (e.g., central platform, decentralized platform) when subcomponents are near their efficiency threshold, and automatically delivers replacement subcomponents. The subcomponents that are near their efficiency threshold can then be retrieved for benchmarking and overhaul. Overhauled components can then be stored in inventory (e.g., as in a subcomponent marketplace), and in the future, provided for replacement of other subcomponents that are near their efficiency thresholds. Benefits of the plan include: lower hydraulic parts cost, predictable budgeting, optimization of apparatus performance by providing real-time monitoring and pre-emptive servicing, elimination of troubleshooting labor costs, and improvements to safety and environmental issues (e.g., by ensuring safe operating parameters, elimination of oil spills, elimination of environmental impacts due to proactive maintenance, etc.). An example of a service flow according to Step S150 is shown in FIGURE 4.

[0091] Other recommended actions and execution of recommended actions are provided in Applications incorporated by reference above.2.6 Method - Reliability Recommendations, Therapeutic Interventions, andOptimization for Groups of ApparatusesFPAI-P07

[0092] Step S240 recites: returning a recommended action for increasing or optimizing reliability of the set of apparatuses, based upon the set of statuses S240, and Step S250 recites: optionally, executing the recommended action.

[0093] With identification of subcomponents that are near the ends of their serviceable lives and / or are past serviceable lives (e.g., based upon life estimates described above), recommended and executed actions can include automatically initiating procedures for providing a replacement subcomponent. Providing a replacement subcomponent can be performed with or without the operator or maintainer's input. Providing a replacement subcomponent can be performed with the operator or maintainer's authorization. In an example, Steps S140 and S150 can include generating an edge-deployed set of subcomponent statuses without disassembling the apparatus, and based upon a fault state of a subcomponent status, generating instructions for ordering and delivering a replacement subcomponent.

[0094] In an example, returning a recommended action and executing the recommended action can include providing replacement subcomponents (e.g., unlimited replacement subcomponents) to an owner, operator, or maintainer of a group of apparatuses (e.g., at a low fixed cost, according to a subscription model, etc.). In an example, a service plan can provide unlimited, optimally timed replacement components for the group of apparatuses, based upon real-time apparatus monitoring as described. Returned subcomponent statuses notify a platform (e.g., central platform, decentralized platform) when subcomponents are near their efficiency threshold, and automatically delivers replacement subcomponents. The subcomponents that are near their efficiency threshold can then be retrieved for benchmarking and overhaul. Overhauled components can then be stored in inventory (e.g., as in a subcomponent marketplace), and in the future, provided for replacement of other subcomponents that are near their efficiency thresholds. Benefits of the plan include: lower hydraulic parts cost, predictable budgeting, optimization of apparatus performance by providing real-time monitoring and pre-emptive servicing, elimination of troubleshooting labor costs, and improvements to safety and environmental issues (e.g., by ensuring safe operating parameters, elimination of oil spills, elimination of environmental impacts due to proactive maintenance, etc.). An example of a service flowFPAI-P07 according to Step S250 is shown in FIGURE 4. As such, an action can include initiating at least one of repair and replacement of a subcomponent of the set of subcomponents

[0095] In some embodiments, where it may take time to deliver a replacement subcomponent, Step S250 can include harvesting or redistributing subcomponents (e.g., based upon subcomponent redundancies) across units of apparatuses of a group, such that executed actions account for the fleet as a whole, with the goal of keeping the entire group of apparatuses operating as optimally as possible. In one scenario, Step S250 can include harvesting of subcomponents of one vehicle of the fleet to ensure proper operation of the remainder of the fleet, where removing the harvested vehicle from service produces a "greater good" solution that maintains the highest level of performance of the fleet. In another scenario, Step S250 can include removing a redundant subcomponent that is not near its serviceable life from one apparatus, to be installed in another apparatus of a group of apparatuses, to maintain optimal performance across the group of apparatuses. The examples of step S250 described here offer a temporary solution to maintaining operations with the highest performance possible across the group of apparatuses, until replacement subcomponents can be delivered.

[0096] Exemplary interfaces for returning subcomponent statuses are shown in FIGURE 5A and FIGURE 5B with respect to serviceable lives. Interfaces can also indicate cost optimizations based upon predictive failure mode determinations, and / or provide realtime monitoring.2.7 Service Example: Automated Component Replacement through a Monitoring and Delivery Platform

[0097] As shown in FIGURE 6A, the system 100 can include or be associated with the mobile device 3, a manager device 4, and a delivery device 5, which can be used to provide notifications and to coordinate delivery of replacement components in response to aspects of the analysis (e.g., aspects that indicate pump, apparatus, and / or subcomponent faults). The system 100 and associated method aspects described below can thus be used to provide the following:

[0098] Automated Service Integration: The system and / or methods involve seamless connections with service shops (e.g., via a cloud-based platform). When the component'sFPAI-P07 monitored condition indicates that replacement is necessary, the system 100 and methods involve automatically notifying the connected service shop for scheduling of service and / or part replacement. The necessary replacement component is pre-ordered and shipped directly to the service provider by way of a delivery application executing on the delivery device 5.

[0099] Efficiency for Service Shops: Service providers benefit from service integration by receiving detailed work orders in advance. As such, service shops know precisely when a component needs replacement and can plan their workload accordingly, reducing wait times and improving service efficiency. The diagnostics provided by the system 100 and methods described also ensure that technicians are fully informed about the condition of the component, streamlining the replacement process.

[0100] Sales and Inventory Management: For manufacturers and distributors, the system 100 and methods provide an automated channel for selling replacement components. As the system architecture predicts a need for a replacement, it automatically triggers an order directly, ensuring that the right components are always available in the right quantities, minimizing inventory costs, and maximizing sales opportunities.

[0101] In relation to executed actions related to component replacement through a monitoring and delivery platform, Steps related to S140 / S150, S240 / S250, and S340 / S350 can include (as shown in FIGURE 6B): providing a digital storefront application to a manager device of a digital storefront S351, establishing communications with a digital storefront through the manager device and the processing subsystem S352, accessing inventory records for a replacement component option available through the digital storefront S353, generating electronic instructions for preparing the replacement component option for transport S354, receiving a notification, transmitted from the digital storefront application to the processing subsystem, that the replacement component option is ready for retrieval S355, providing a delivery application to a delivery device associated with a transporter S356, establishing communications with the delivery application through the delivery device S357, and generating electronic instructions for transportation and delivery of the replacement component option to an intended recipient (e.g., user) of the replacement component option S358.FPAI-P07

[0102] In relation to providing insights to a user of the component (e.g., pump component) or apparatus, and in relation to initiating delivery of replacement components, Steps S340 and S350 can include (as shown in FIGURE 8C): providing a monitoring application to the mobile device associated with a user of the battery S361, receiving a request from the user, through the monitoring application, the request indicating intent to order the replacement component option S362, transmitting status updates to the user, through the monitoring application, regarding a delivery status of the replacement component option S363, in coordination with Steps S351-S358 above.

[0103] In more detail, one example of the mobile application can include functionality for: a user to add a vehicle identification number (VIN) to the user’s account (e.g., using a camera of the mobile device, by manual entry, etc.), where the VIN is associated with a vehicle using the component; providing notifications to the user (e.g., notifications regarding a detected faulty component associated with one or more user apparatuses, notifications regarding an available replacement component or component, notifications regarding a discount or coupon for a replacement component or component, etc.); receiving transmissions pertaining to battery health alerts, proactive replacement of batteries having usage above a threshold in relation to a lifespan of the respective batteries, wireless pairing with the battery through system 100 described above (e.g., through Bluetooth™ connectivity, WiFi connectivity, another mode of wireless connectivity, etc.); logging into a user account; registering a new user account; detecting components and / or apparatuses in proximity to the mobile device (e.g., through Bluetooth™ connectivity, WiFi connectivity, another mode of wireless connectivity); “tapping” to connect with the component (through a near field communication mechanism, through a radiofrequency communication mechanism, etc.); providing notifications regarding battery status, with rendered graphics (e.g., indicating a component health rating numerically); requesting a replacement component; providing delivery information for the replacement component; identifying a nearby location where a replacement component can be retrieved; providing details of an order for a replacement component; entering information for a user profile; providing a digital shopping cart with checkout functionality; providing replacement componentFPAI-P07 information (e.g., technical specifications, compatible vehicles, compatible apparatuses, etc.); and / or other suitable functionality.

[0104] In more detail, one example of the driver application can include functionality for: transitioning between an offline status and an online status (e.g., with respect to availability for retrieval and delivery of a replacement component corresponding to a user order); logging into a driver account; observing all suitable candidate orders for delivery of a replacement component or subcomponent (e.g., based upon proximity to the driver, etc.); selecting one or more orders to deliver; providing navigation instructions to a delivery location; marking an order as delivered; cancelling delivery of an order; entering information for a user profile; entering information for receiving payment for delivering an order; and / or other suitable functionality.

[0105] In more detail, one example of the digital storefront application can include functionality for: logging into a manager account; receiving information rendered at a display regarding active orders during a time window, completed orders during a time window, picked up orders during a time window, unassigned orders for delivery, available drivers / transporters, and other storefront information; assigning a driver / transporter to an order for delivery; cancelling an order; providing a refund for an order; receiving notifications related to a driver status (e.g., arrival time at the location of the replacement battery, estimated delivery time, delivery status, etc.); changing drivers / transporters associated with one or more orders; accepting orders; marking orders as completed; receiving information regarding a fleet of available drivers / transporters; observing driver / transporter profiles; receiving order notifications; and / or other suitable functionality.3. System

[0106] As shown in FIGURE 7, an embodiment of a system 600 for evaluating apparatus events includes: a pump 5, a sensor subsystem 610 (e.g., sensor cluster) coupled to the pump 5 and including one or more of: a pressure sensor 612, a temperature sensor 614, a flow sensor 616, and a pump demand sensor 618; an interface 620 between the sensor subsystem 610 and pump 5 with an apparatus 10; a monitor 630 (e.g., with communicationFPAI-P07 and / or processing architecture) coupled to the sensor subsystem 610 and configured to receive outputs of the sensor subsystem 610; and a processing subsystem 640 (integrated with or distinct from monitor 630) and including non- transitory media storing instructions that, when executed by the processing subsystem 140, perform operations for identifying, from outputs of the monitor 630 / sensor subsystem 610, a set of signatures corresponding to states and events of the apparatus 10. The set of signatures are then used by the processing subsystem 640 to execute actions configured to respond to the states / events appropriately, thereby improving performance of the apparatus (e.g., in terms of output, in terms of efficiency, in terms of correcting undesired statuses, in terms of responding to failure modes, etc.).

[0107] The system 600 functions to provide improved tools for monitoring, forecasting, and troubleshooting events (e.g., failure modes, lifespans, etc.) of hydraulic apparatus components at global and subcomponent levels. In particular, in applications where the system 600 is coupled to vehicles (e.g., in the trucking industry, aerial vehicles, watercraft, etc.) and / or in other industrial applications, the system 600 can enable assessments of hydraulic apparatus health and other anticipated events at subcomponent and global levels, in an improved manner. Subcomponents monitored by the system 600 can be upstream of the pump, at the pump, downstream of the pump, and / or associated with the pump, and in examples, can include any one or more of: valve components, pump oil, piston components, motor components, or other suitable apparatus subcomponents. The system 100 can also generate usage information from signals processed, at global and subcomponent levels of abstraction. The pump 5 can be a hydraulic pump or another suitable type of pump. In examples, the pump 5 can be a: centrifugal pump, a positive displacement pump (e.g., gear pump, diaphragm pump, and piston pump, etc.), peristaltic pump, screw pump, jet pump, vacuum pump, and / or other suitable type of pump.

[0108] Such inventions associated with the system 600 thus prevent unexpected and unplanned maintenance events which have significant associated costs, thereby improving hydraulic system performance and / or allow hydraulic systems to have extended lifespans of use. Additionally, the system 600 functions to analyze individual subcomponents of apparatus with a single set of sensors coupled to the apparatus at a single position, therebyFPAI-P07 enabling operators to obtain operating life, health, remaining life, and / or other statuses of individual subcomponents in a manner that is significantly more efficient and lower in cost. In particular, the set of sensors can be used to assess system statuses and events upstream and / or downstream of the position of coupling between the sensors and the apparatus, in a manner that has not previously been achieved. Furthermore, the system 600 functions to rapidly process signal streams, with use of neural network architecture to extract insights related to hydraulic apparatus statuses and events.

[0109] In specific examples, the system 600 provides innovation in advanced sensor system design and machine learning approaches, in fields using systems, to provide a plug- and-play, real-time monitoring and predictive maintenance solution in a cost-effective manner. In particular, the system 600 monitors a small number of system parameters and transmits data for processing (e.g., at least in part in cloud computing systems, in coordination between cloud and non-cloud based computing systems) by encoding physics of pump and / or apparatus operation, in order to determine health statuses of the associated apparatuses, at the component level, in real time. Apparatus statuses and actionable alerts are the provided by the system 600 to the user (e.g., using a customer portal), to enable them to streamline their equipment maintenance procedures and prevent downtime.

[0110] In embodiments, the system 600 can be configured to perform steps of embodiments, variations, and examples of any methods described herein. However, the system 600 can additionally or alternatively be configured to perform other suitable methods.

[0111] Further details of components of the system 100 are described in the following sections.3.1 System - Sensor Cluster

[0112] As shown in FIGURE 7, the system 100 includes a sensor subsystem 610 including one or more of: a pressure sensor 612, a temperature sensor 614, a flow sensor 616, and a pump demand sensor 618. The sensors of the sensor subsystem 610 collectively function to generate signals for monitoring a set (e.g., minimized set, discrete set, etc.) of signal types and / or number of a parameters, from which performance of the apparatus and / or demand on the apparatus can be extracted. The data can then be processed accordingFPAI-P07 to methods described in more detail below, in order to efficiently assess statuses of and / or anticipate events of the apparatus and its subcomponents. As described above and further below, the set of sensors 610 can include: a pressure sensor 612, a temperature sensor 614, a flow sensor 616, and a pump demand sensor 618.

[0113] The pressure sensor 612 functions to detect the pressure of any fluid of the pump or apparatus (e.g., at the pump 5 outlet, as described in relation to the interface 120 below). In embodiments, the pressure sensor 612 converts sensed pressures into electrical signals (e.g., analog electrical signals) for reception and / or pre-processing by the monitor 630. In variations, the pressure sensor 612 can include components (e.g., diaphragms, other components) in contact or communication with hydraulic fluid of the hydraulic apparatus, thereby enabling direct sensing of pressure of the fluid. Alternatively, in other variations, the pressure sensor 612 may not include components that are directly in contact with hydraulic fluid, and instead operate in another manner (e.g., by measuring deformation of a component contacting the hydraulic fluid). In a specific example, the pressure sensor 612 can include a strain gauge coupled to an output line or other conduit associated with hydraulic fluid of the hydraulic apparatus, and measure deformation (e.g., circumferential deformation, longitudinal deformation, etc.) of the conduit as an indirect measure of internal pressure.

[0114] The temperature sensor 614 functions to measure the temperature of the fluid of the apparatus and / or pump. Similar to the pressure sensor 612, the temperature sensor 614 can include components (e.g., probes, other components) in contact or thermal communication with the apparatus and / or pump. Alternatively, in other variations, the temperature sensor 614 may not include components that are directly in contact with fluid, and instead operate in another manner (e.g., by measuring temperature of a component contacting the fluid, by measuring temperature of a component where the temperature of the component is a function of the temperature of the fluid).

[0115] The flow sensor 616 functions to measure flow characteristics of the apparatus(e.g., at the main output of the pump 5 of the apparatus) by generating electrical signals (e.g., voltage signals with waveform characteristics) that are proportional to flow rate and other characteristics. The flow sensor 616 can include components that are inline with flowFPAI-P07 through the hydraulic apparatus (e.g., inline with the pump 5 output). Additionally or alternatively, the flow sensor 616 can include components that determine flow characteristics from differential pressures (e.g., by measuring pressure differentials across a known junction geometry). Still alternatively, the flow sensor 116 can include non-contact sensing components for measuring flow characteristics. In examples, non-contact flow sensors can detect flow using ultrasonic, electromagnetic, or capacitive technologies, allowing them to measure the movement of fluids without any direct intrusion. As such, the flow sensor 616 can detect flow aspects without being positioned within a flow channel of the pump 5.

[0116] The pump demand sensor 618 functions to measure varying pump demand for the apparatus. The pump demand sensor 618 can measure pump demand in relation to operational demand of the hydraulic apparatus, in terms of revolutions per minute (RPM) or another suitable measure. In particular, in contrast to some industrial applications where RPM of the pump 5 of the apparatus is constant at the synchronous electric motor speeds, mobile applications of use (e.g., in relation to trucking and other mobile applications), where the pump 5 of the apparatus is coupled directly to the engine or transmission, can require variable engine speeds, which causes pump 5 flow to vary.

[0117] In variations, sensors of the sensor subsystem 610 can be isolated from each other or otherwise configured to prevent undesired signal interference or crosstalk, thereby improving quality of training data, test data, and data processed during normal operation of the system 100. Outputs can further be isolated from each other in relation to architecture of electronics of the monitor 630 and / or processing subsystem 640 described below.

[0118] While a unit of each sensor type is described above, in variations of the system 600, the sensor subsystem 610 can include multiple units of each sensor type and / or other types of sensors. Furthermore, the sensor(s) of various types can be positioned to interrogate fluid flow at identical positions of the hydraulic apparatus (e.g., for sensor redundancy), or alternatively, can be configured to interrogate fluid flow at different positions of the hydraulic apparatus in order to generate measurements for comparative analyses, or analyses requiring differential measurements.FPAI-P07

[0119] In variations, the sensor subsystem 610 can include sensor types not described above. For instance, the sensor subsystem no can include one or more of: sensors for filter heads of the apparatus, in order to detect when a filter replacement is needed (e.g., where the filter head sensors monitor a pressure differential switch at the filter head); magnetic sensors (e.g., Hall Effect sensors) used to measure hydraulic motor RPM or other linear / angular position sensors for detection of hydraulic actuator motion; other linear position sensors (e.g., linear encoders); other angular motion sensors (e.g., angular encoders); weight sensors (e.g., to interrogate vehicle load and distributions or other loads / distributions associated with non-vehicle applications); ultrasonic / vibration sensors coupled to one or more motors and / or cylinders associated with the apparatus; and other sensor types.

[0120] The sensor subsystem 610 can additionally or alternatively omit one or more sensor types listed above. For instance, in relation to the processing subsystem 640 described in more detail below, the sensor subsystem 610 can omit sensor types as required, in relation to assessing apparatus subcomponent statuses and / or events with the fewest number of signal types required.3.2 System - Sensor Interface to Pump

[0121] As shown in FIGURE 7, the system 600 includes an interface 620 between the sensor subsystem 610 and a hydraulic pump 5 of a hydraulic apparatus 10, where the interface 620 functions to provide a mechanism for robust physical coupling of the sensor subsystem 610 to the pump 5. Additionally, the interface 620 is configured to facilitate rapid and efficient installation, such that the system 600 can be coupled universally to system main pumps in a brand and equipment agnostic manner.

[0122] In variations, the interface 620 can couple the sensor subsystem 610 and pump5 to the apparatus 10. In variations, the interface 620 couples the sensor subsystem 610 to the main output of the pump 5; however, in other variations, the interface 620 can couple the sensor subsystem 610 to another suitable portion (e.g., inlet, valve position, etc.) of the pump 5, in order to detect signals for downstream processing.

[0123] In order to provide a robust mechanism of coupling with the pump 5, the housing 621 of the interface 620 can be composed of a material having suitable mechanicalFPAI-P07 properties. In variations, materials of the housing 121 and / or other aspects of the interface 120 can be configured to provide suitable mechanical properties in relation to stresses attributed to flow through the interface (e.g., radial stresses, shear stresses, longitudinal stresses, tensile stresses, compressive stresses, stresses associated with pressure vessels; stresses associated with impacts to the system no and / or the hydraulic apparatus during use; stresses due to thermal expansion; stresses due to thermal contraction; and other associated stresses depending upon applications of use.

[0124] Additionally or alternatively, the housing 621 of the interface 620 can be composed of a material having suitable thermal properties. In variations, materials of the housing 621 and / or other aspects of the interface 620 can be configured to provide suitable thermal properties in relation to one or more of: thermal conductivity (e.g., in relation to allowing proper operation of the temperature sensor 614, in relation to insulative behavior), thermal expansion (e.g., in relation to having a desired level of thermal expansion); and other thermal properties depending upon application of use.

[0125] Additionally or alternatively, the housing 621 of the interface 620 can be composed of a material having suitable physical or surface properties. In variations, materials of the housing 621 and / or other aspects of the interface 620 can be configured to provide suitable physical or surface properties in relation to one or more of: electrochemical properties (e.g., due to corrosive environments), electromagnetic properties (e.g., in relation to ultraviolet light exposure), and other surface or physical properties due to environment of use of the system 600.

[0126] The housing 621 can also function to provide a seal about sensitive electronic and other components associated with the sensor subsystem 610 (e.g., such as the monitor 630 described in more detail below). In variations, the seal can be a hermetic seal, a seal that allows passage of gasses but prevents passage of liquids, or another suitable type of seal. In these variations, the housing 621 can function as a seal, or can additionally or alternatively include sealing components (e.g., gaskets, o-rings, sealing compounds, etc.) at openings of the housing 621, or between various sub-portions of the housing 621.

[0127] In specific examples, the housing 621 can be composed of a metallic material (e.g., aluminum), a metal-derived material, a ceramic material, a natural material, aFPAI-P07 synthetic material, or another suitable material. In other specific examples, the housing 121 can be composed of a polymeric material having suitable properties.

[0128] As described above, the interface 620 can also couple the sensor subsystem 610 to other portions of the apparatus (e.g., in relation to mobile applications, in relation to industrial applications). In particular, the interface 620 can allow the pump demand sensor 618 to couple to a vehicle interface (e.g., of a vehicle comprising the apparatus) for monitoring engine RPM or other operational demand characteristics. In variations, the interface 620 can allow the sensor subsystem 610 to couple to a vehicle interface / CAN bus. However, other variations of the interface 620 can allow the sensor subsystem 610 to couple to another type of vehicle interface or other type of hydraulic apparatus interface, with another suitable protocol.

[0129] As noted above, the interface 620 can additionally or alternatively provide “contactless” coupling between the sensor subsystem 610 and portions of the apparatus 10 (e.g., at the pump 5, away from the pump 5). In variations, the interface 620 can thus include architecture for enabling non-contact sensing between sensors of the sensor subsystem 610 and the apparatus, with architecture for wireless signal transmission between various system components. A variation of non-contact sensing can employ a clamp or other coupler that couples the non-contact sensors to the pump (e.g., near the main output, near another pump component, near another component of the hydraulic apparatus).3.3 System - Monitor

[0130] As shown in FIGURE 7, the system 600 includes a monitor 630 coupled to the sensor subsystem 610 (e.g., through interface 620, proximal to interface 620, within the housing 621 associated with interface 620, etc.) and configured to receive outputs of the sensor subsystem 610 and to transmit data derived from outputs of the sensor subsystem 610 to components of the processing subsystem 640 described in more detail below. In more detail, the monitor 630 can include a controller (e.g., an embedded control module) that samples data generated from the sensor subsystem 610, logs the data, and transmits the data (e.g., over a wireless connection, over a wired connection) to the processing subsystem 640 described below.FPAI-P07

[0131] In variations, the monitor 630 can be configured to receive analog signals from the sensor subsystem 610. Additionally or alternatively, the monitor 630 can be configured to receive digital signals (e.g., signals that are digitized from analog signals, etc.) from the sensor subsystem 610. As such, signals from the sensor subsystem 110 can be transmitted to the monitor 620 as analog signals, or alternatively as digital signals (e.g., digitized from analog signals, etc.), and the monitor 620 can accordingly process and transmit data derived from the signals to the processing subsystem 640.

[0132] In a variations, the monitor 630 can include computer architecture and circuitry for one or more of: connectors between various sensor and monitor 630 components (e.g., in relation to sensor inputs to the monitor 630), data storage, power source (e.g., battery, other power source), power management, and data transmission (e.g., via wireless communications, via wired communications, etc.). The monitor 630 can additionally or alternatively include architecture and circuitry for preconditioning of signals and / or data derived from the sensor subsystem 610 and / or other suitable functions.3.4 System - Processing Components

[0133] As shown in FIGURE 7, the system includes a processing subsystem 640 operatively coupled to or integrated with the monitor 630 and / or sensor subsystem 610 and including non-transitory computer-readable media storing instructions that, when executed by the processing subsystem 640, perform operations for identifying, from outputs of the monitor 630, a set of unique signatures corresponding to states and events of the apparatus 10. The processing subsystem 640 thus functions to generate analysis (e.g., related to apparatus and subcomponent statuses and events) derived from signals of the sensor subsystem 610, and to facilitate execution of response actions to improve or maintain apparatus performance. As such, processing and communications subsystem components can comprise instructions for returning an analysis indicative of the set of states of the set of subcomponents. In examples, an analysis can include information pertaining to at least one of: a cavitation event, a leakage event, and a wear event of a subcomponent of the set of subcomponents. In relation to generation of analyses, the processing subsystem 640 can also function to generate and process training and test datasets for development and refinementFPAI-P07 of machine learning (ML) models, where the ML models return outputs associated with apparatus and subcomponent statuses and events from received sensor data.

[0134] The processing subsystem 640 can include one or more processing subsystems implemented in one or more of: a remote server, a personal computer, a cloud-based computing system (e.g., Zeek™ platform, Amazon™ Web Services (AWS) platform, etc.), a computing module of a mobile electronic device (e.g., mobile communication device, wearable computing device, etc.), and any other suitable computing device. The processing subsystem 640 can communicate with other system components (e.g., monitor 630) and / or third party systems over a network, in relation to data transfer or other operations (e.g., some of which are described above). Furthermore, architecture of the processing subsystem 640 can overlap with that of the monitor 630, and / or be physically distinct from that of the monitor. For instance, in one variation, some components of the processing subsystem 640 can be on-board the monitor 630, and / or some components of the processing subsystem 640 can be distinct and implemented in the cloud and / or computing systems remote from the monitor 630.

[0135] In more detail, the network functions to enable data transmission between system components and / or third party platforms. The network can include a combination of one or more of local area networks and wide area networks, and / or can include wired and / or wireless connections to the network. The network can implement communication linking technologies including one or more of: 802.11 architecture (e.g., Wi-Fi, etc.), 3G architecture, 4G architecture, 5G architecture, Ethernet, worldwide interoperability for microwave access (WiMAX), long term evolution (LTE) architecture, code division multiple access (CDMA) systems, digital subscriber line (DSL) architecture, and any other suitable technologies for data transmission.

[0136] In variations, the network can be configured for implementation of networking protocols and / or formats including one or more of: hypertext transport protocol (HTTP), multiprotocol label switching (MPLS), transmission control protocol / Internet protocol (TCP / IP), file transfer protocol (FTP), simple mail transfer protocol (SMTP), hypertext markup language (HTML), extensive markup language (XML), and any other suitable protocol / format. The network 150 can also be configured for and / or provide, throughFPAI-P07 communication links, encryption protocols for improving security of data transmitted over the network.

[0137] As noted above, the processing subsystem 640 is configured to perform operations for identifying, from outputs of the monitor 630, a set of unique signatures corresponding to states and events of the hydraulic apparatus 10. In variations, the operations can involve processes associated with one or more of: receiving a dataset derived from outputs of the sensor subsystem 610; performing a set of transformation operations upon the dataset, using a neural processing unit (NPU) of the monitor 630 / processing system 640 (e.g., as a processing and communications subsystem); identifying a set of signatures corresponding to a set of states of a set of subcomponents of at least one of the apparatus 10 and the pump 5, from the set of transformation operations, wherein the set of states of the set of subcomponents comprise fault states of the set of subcomponents attributed to the set of signatures; and executing an action for improving or maintaining proper performance of at least one of the apparatus 10 and the pump 5, based upon the set of signatures

[0138] One or more operations executed in coordination with the processing subsystem 140 can implement ML models (e.g., classifiers) refined with training data generated by the system 600 and / or other systems, as described in relation to the methods described.

[0139] Embodiments, variations, and examples of system aspects are described an shown in one or more of: U.S. Application No. 16 / 939,026 filed on 26-JUL-2020 and now issued as U.S. Pat. No. 10,962,955 on 30-MAR-2021; U.S. Application No. 18 / 333,037 filed on 12-JUN-2023; U.S. Application No. 18 / 342,525 filed on 27-JUN-2023; and U.S. Application No. 18 / 497,913 filed on 27-JUN-2023, which are each incorporated in its entirety by reference above.

[0140] The system embodiment(s) can, however, be configured to implement other workflows including variations of those described, and / or other workflows.4. Computer Systems

[0141] The present disclosure provides computing and control subsystems that are programmed to implement methods associated with the monitoring and prediction devicesFPAI-P07 described. FIGURE 7 shows a computing and control subsystem 1001 that is programmed or otherwise configured to, for example, provide monitoring capabilities for apparatuses described.

[0142] The computing and control subsystem 1001 includes architecture for processing and transmitting data (e.g., pressure data, flow data, temperature data, pump demand data, etc.) detected from said interface(s) to said apparatuses.

[0143] The computing and control subsystem 1001 can include a processing unit (neural and / or central processing unit) 1005, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computing and control subsystem 1001 also includes memory or memory location 1010 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 1015, communication interface 1020 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1025, such as cache, other memory, data storage and / or electronic display adapters. The memory 1010, storage unit 1015, interface 1020 and peripheral devices 1025 are in communication with the processing unit 1005 through a communication bus (solid lines), such as a motherboard. The storage unit 1015 can be a data storage unit (or data repository) for storing data. The computer system 1001 can be operatively coupled to a computer network (“network”) 1030 with the aid of the communication interface 1020, but also diagnose and generate outputs on-chip. The network 1030 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet.

[0144] In some embodiments, the network 1030 is a telecommunication and / or data network. The network 1030 can include one or more computer servers, which can enable distributed computing, such as cloud computing. For example, one or more computer servers may enable cloud computing over the network 1030 (“the cloud”) to perform various aspects of facilitating charging of an electric vehicle, with desired security, authentication, and locking functionalities associated with various types of charging sessions and / or different users. Such cloud computing maybe provided by cloud computing platforms such as, for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM cloud. In some embodiments, the network 1030, with the aid of the computer systemFPAI-P07 iooi, can implement a peer-to-peer network, which may enable devices coupled to the computer system 1001 to behave as a client or a server.

[0145] The processing unit 1005 can include one or more computer processors and / or one or more neural processing units (NPUs). The processing unit 1005 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 1010. The instructions can be directed to the processing unit 1005, which can subsequently program or otherwise configure the processing unit 1005 to implement methods of the present disclosure. An example of operations of the NPU and / or other processing components is shown in FIGURES 3A and 3B, where the NPU comprises self-attention timeseries transformer architecture comprising an encoder block comprising multi-head attention subarchitecture, and wherein the self-attention time-series transformer architecture of the NPU omits a decoder block. In relation to FIGURES 3A and 3B, the processing components (e.g., NPU, etc.) can further include instructions for processing a signature of the set of signatures with a temporal convolutional network (TCN) structured to process the signature with a main convolutional path and a residual connection path to generate a set of features corresponding to states of the set of subcomponents of the apparatus.

[0146] Examples of operations performed by processing unit 1005 can include fetch, decode, execute, and writeback. The processing unit 1005 can be part of a circuit, such as an integrated circuit. One or more other components of the computing and control subsystem 1001 can be included in the circuit. In some embodiments, the circuit is an application specific integrated circuit (ASIC).

[0147] The storage unit 1015 can store files, such as drivers, libraries and saved programs. The storage unit 1015 can store user data, e.g., user preferences and user programs. In some embodiments, the computer system 1001 can include one or more additional data storage units that are external to the computer system 1001, such as located on a remote server that is in communication with the computer system 1001 through an intranet or the Internet.FPAI-P07

[0148] The computing and control subsystem 1001 can communicate with one or more remote computer systems through the network 1030. For instance, the computer system 1001 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 1001 via the network 1030.

[0149] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computing and control subsystem 1001, such as, for example, on the memory 1010 or electronic storage unit 1015. The machine executable or machine-readable code can be provided in the form of software. During use, the code can be executed by the processor 1005. The code can be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0150] Embodiments of the systems and methods provided herein, such as the computing and control subsystem 1001, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, or disk drives, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, anotherFPAI-P07 type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non- transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0151] Hence, a machine readable medium, such as computer-executable code, may take many forms, including a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as maybe used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0152] The computing and control subsystem 1001 can include or be in communication with an electronic display 1035 that comprises a user interface (UI) 540 for providing, for example, a visual display indicative of statuses associated with charging of an electric vehicle, security information, authentication information, and locking statusesFPAI-P07 associated with various types of charging sessions and / or different users. Examples of UIs include, without limitation, a graphical user interface (GUI) and web-based user interface.

[0153] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 1005. The algorithm can, for example, facilitate charging of an electric vehicle, with desired security, authentication, and locking functionalities associated with various types of charging sessions and / or different users.

[0154] In one set of embodiments, methods implemented by way of or as supported by the computing and control subsystem 1001 can include methods for communication of statuses of subcomponents to another device (e.g., mobile computing device, wearable computing device, other smart device, etc.). Communicated statuses can then be used by the systems described to return notifications (e.g., to an apparatus operator or manager, to another entity) and / or execute other actions pertaining to statuses of the subcomponent(s) of the apparatus(es), where example executed actions can include generation of instructions to control states of the apparatus for safety, or to address faults identified as described above, and / or generation of instructions to control states of the apparatus or perform other recommended actions.

[0155] Additionally or alternatively, the computing and control subsystem 1001 can include architecture with programming to execute other suitable methods.5. Conclusions

[0156] The FIGURES illustrate the architecture, functionality and operation of possible implementations of systems, methods and computer program products according to preferred embodiments, example configurations, and variations thereof. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block can occur out of the order noted in the FIGURES. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchartFPAI-P07 illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0157] As a person skilled in the art will recognize from the previous detailed description and from the figures and claims, modifications and changes can be made to the preferred embodiments of the invention without departing from the scope of this invention defined in the following claims.

Claims

FPAI-P07CLAIMSWhat is claimed is:

1. A method for monitoring an apparatus, the method comprising: establishing an interface between a sensor cluster incorporated into a pump and the apparatus, the interface comprising a housing coupling the sensor cluster to at least one of the pump and the apparatus, wherein the sensor cluster comprises a flow sensor structured for measuring of flow characteristics of the pump; sampling a set of data streams, derived from outputs of the sensor cluster; performing a set of transformation operations upon the set of data streams, wherein the set of transformation operations comprises operations applied by self-attention timeseries transformer architecture; identifying a set of signatures corresponding to states and events of a set of subcomponents of at least one of the pump and the apparatus, from the set of transformation operations, wherein the set of signatures are extracted from outputs from the sensor cluster; and executing an action for improving or maintaining proper performance of at least one of the pump and the apparatus, based upon the set of signatures.

2. The method of Claim 1, wherein the sensor cluster comprises a pressure sensor and a temperature sensor.

3. The method of claim 1, wherein the sensor cluster comprises a pump demand sensor.

4. The method of claim 1, wherein the pump demand sensor is coupled to a vehicle interface of a vehicle comprising the apparatus.

5. The method of claim 1, wherein the set of signatures comprises a signature indicative of a cavitation event involving fluid of the apparatus.FPAI-P076. The method of claim 1, wherein the self-attention time-series transformer architecture comprises an encoder block comprising multi-head attention subarchitecture, wherein the self-attention time-series transformer architecture omits a decoder block.

7. The method of claim 6, further comprising processing a signature of the set of signatures with a temporal convolutional network (TCN) structured to process the signature extracted from the self-attention time-series transformer architecture with a main convolutional path and a residual connection path to generate a set of features corresponding to states of the set of subcomponents of the apparatus.

8. The method of claim 1, wherein the set of transformation operations is performed by a neural processing unit (NPU) coupled to the sensor cluster, wherein the NPU comprises at least 0.5 trillions of operations per second (TOPS) capability with energy use performance of less than 2 picojoule per operation.

9. The method of claim 1, wherein the set of signatures comprises a signature indicative of a wear event and a cause of the wear event of a subcomponent of the apparatus.

10. The method of claim 1, wherein the set of signatures comprises a signature indicative of a leakage event and a cause of the leakage event of a subcomponent of the apparatus.

11. The method of claim 1, wherein executing the action comprises initiating at least one of repair and replacement of a subcomponent of the set of subcomponents.

12. A system for monitoring a apparatus, the system comprising:FPAI-P07 a pump; a sensor cluster comprising a pressure sensor, a temperature sensor, and a flow sensor, wherein the sensor cluster is coupled to the pump and is surrounded by a housing that interfaces the sensor cluster with at least one of the pump and the apparatus; a monitor coupled to the sensor cluster and comprising a processing and communications subsystem for sampling data derived from the sensor cluster and transmitting data from the processing and communications subsystem, wherein the processing and communications subsystem comprises a non-transitory computer-readable medium comprising instructions stored thereon, that when executed by the processing subsystem perform one or more steps of: receiving a dataset derived from outputs of the sensor cluster; performing a set of transformation operations upon the dataset, using a neural processing unit (NPU) of the processing and communications subsystem; identifying a set of signatures corresponding to a set of states of a set of subcomponents of at least one of the apparatus and the pump, from the set of transformation operations, wherein the set of states of the set of subcomponents comprise fault states of the set of subcomponents attributed to the set of signatures; and executing an action for improving or maintaining proper performance of at least one of the apparatus and the pump, based upon the set of signatures.

13. The system of claim 12, wherein the pump comprises a hydraulic pump.

14. The system of claim 12, wherein the sensor cluster comprises a pump demand sensor coupled to a vehicle interface of a vehicle comprising the apparatus.

15. The system of claim 12, wherein the NPU comprises self-attention time-series transformer architecture comprising an encoder block comprising multi-head attention subarchitecture, and wherein the self-attention time-series transformer architecture of the NPU omits a decoder block.FPAI-P0716. The system of claim 12, wherein the processing and communications subsystem further comprises instructions for processing a signature of the set of signatures with a temporal convolutional network (TCN) structured to process the signature with a main convolutional path and a residual connection path to generate a set of features corresponding to states of the set of subcomponents of the apparatus.

17. The system of claim 12, wherein the processing and communications subsystem comprises further comprises instructions for returning an analysis indicative of the set of states of the set of subcomponents.

18. The system of claim 17, wherein the set of subcomponents comprises a fluid component, a filter component, an actuator component, a cylinder component, a valve component, and a pump component.

19. The system of claim 17, wherein the analysis comprises information pertaining to at least one of: a cavitation event, a leakage event, and a wear event of a subcomponent of the set of subcomponents.

20. The system of claim 12, wherein the flow sensor comprises a non-contact flow sensor configured to detect flow through the pump without being positioned within a flow channel of the pump.

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