40 results about "Proactive maintenance" patented technology

The invention discloses a bridge girder erection machine structure health intelligent management system and an intelligent management method thereof. The system comprises a state data acquisition unit, a data preprocessing module, a structure state modeling module, a state prediction and residual life estimation module, an early warning and control module, a self-learning and optimization module and a system integration module. The method comprises sensor deployment, data preprocessing, structural state modeling, state prediction and residual life estimation, early warning and control, self-learning and optimization and system integration. Structural state monitoring data are collected in real time through a sensor, a structural state model is constructed after preprocessing, a future state is predicted in combination with time sequence prediction and a mechanism model, and the remaining safe life and risk points are estimated. And the system triggers multi-stage early warning according to a prediction result, executes an active maintenance control strategy, and continuously improves a prediction model through a self-learning and optimization module. According to the invention, real-time health management, remote operation and full-life-cycle risk control of the bridge girder erection machine structure can be realized.

System and methods are disclosed relating to cloud edge based anomaly detection. In an example, an edge monitoring node can include a thermal camera to capture a thermal image of equipment that is under monitoring for an an anomaly event. The node further includes a machine learning (ML) model to process the thermal image of the equipment to detect the anomaly event. The node further includes a network interface to communicate the detected anomaly event over a network to a remote computing platform to determine one or more recommendations for proactive maintenance of the equipment.

The invention provides a GPU (Graphics Processing Unit) hardware health prediction and active maintenance method and system, and belongs to the technical field of computing hardware maintenance and fault prediction. Based on the GPU health state data, a health degree comprehensive score of the GPU is calculated by using a preset health degree evaluation model, and a key health index in a future set time period is predicted by using a time sequence model. Calculating the fault probability of a preset type of fault risk by using a preset fault risk model based on the health degree comprehensive score of the GPU and / or the predicted key health index; and comparing the calculated fault risk probability with a preset threshold value, and executing a main preset maintenance action when the fault risk probability exceeds the preset threshold value. Through multi-source data acquisition and time sequence prediction, the potential fault risk of the GPU is found in advance, the maintenance action is actively executed, task interruption and data loss are reduced, and the reliability and availability of a GPU cluster are improved.

The present invention introduces an advanced system and method for predictive maintenance and fault detection, leveraging the synergistic potential of quantum computing and graph transformer networks. This innovation collects and preprocesses data from diverse sources through edge computing, enriching this data with supplemental information to construct a comprehensive operational dataset. Utilizing an ontology-based framework, the system organizes the data into a knowledge graph, which is then analyzed using quantum computing techniques to uncover complex, correlated relationships. The extracted relationships are further analyzed by a Graph Transformer Network (GTN) equipped with a multi-head attention mechanism, enabling the identification of spatio-temporal patterns indicative of potential system faults. The system classifies these patterns to distinguish between normal operation, potential faults, and outliers, facilitating proactive maintenance actions. This invention represents a significant advancement in the field of predictive maintenance, offering improved reliability, efficiency, and operational insight for complex systems.

The invention relates to an operation management system and method, and relates to the technical field of intelligent operation management, and the system comprises a data access and standardization unit which carries out the unified access and standardization processing of multi-source heterogeneous operation data including cross-brand and cross-protocol equipment, and lays a high-quality data foundation for subsequent analysis; the intelligent analysis and prediction unit is used for carrying out deep analysis by utilizing an advanced algorithm model based on the standardized data, outputting an accurate prediction result and business insight, and constructing a dynamic knowledge base; and the intelligent strategy making and executing unit is used for making and automatically executing operation strategies such as predictive maintenance and dynamic resource optimization scheduling according to a prediction result and knowledge base insight and in combination with an optimization target, and feeding back a strategy execution effect to realize closed-loop learning and system self-adaption. According to the method, the operation efficiency and the resource utilization level can be effectively improved, the equipment reliability and the active maintenance capability are enhanced, and prospective intelligent operation management is realized.

Systems and methods are directed to increasing the reliability and resilience of power grids, solar farms, and other energy sources, by predicting fault events before they occur and taking preventive action, thus avoiding system down time. In accordance with embodiments, a method of responding to pre-fault events in an electrical system includes monitoring electrical signals generated by the system; comparing the electrical signals to fault signatures; in response to the comparison, triggering a maintenance event, such as dispatching maintenance personnel, automatically disconnecting a component predicted to fail within a pre-determined time limit, or powering down the system, to name only a few examples. The fault signatures can be generated using artificial intelligence.

System and method for automatic selection of training region selection for deep learning model is disclosed. An identification module 110 identifies equipment shutdowns and excludes buffer region data. A segmentation module 120 estimates operational regions and divides data into predefined operating regions using K-means clustering. A feature analysis module 130 determines top contributing tags and identifies low and high values within operational regions. A domain comparison module 140 compares contributing tags with domain knowledge, excluding regions associated with mapped failures. A data cleaning module 150 ensures data integrity by recalculating statistical measures and eliminating outliers. A filtering module 160 refines data and consolidates regions to reduce training ranges. This integrated approach enhances equipment monitoring and predictive analytics, enabling proactive maintenance strategies and optimizing operational efficiency.

An intelligent dosing platform system for device lifecycle management and predictive maintenance comprising intelligent injection devices with sensors configured to detect operational parameters, device monitoring modules tracking sensor calibration state, battery life metrics, and device performance data. A predictive analytics module analyzes device operational data to forecast maintenance timing, sensor calibration requirements, and device replacement needs. The system includes maintenance scheduling modules that generate automated maintenance alerts based on predicted device performance degradation. A lifecycle documentation module maintains comprehensive device history records including deployment dates, calibration events, maintenance activities, and retirement tracking. The system optimizes device fleet performance through proactive maintenance scheduling, minimizes device downtime through predictive failure prevention, ensures patient safety through continuous device integrity monitoring, and maintains regulatory compliance through comprehensive lifecycle documentation and firmware management capabilities.

The invention relates to the technical field of motor fatigue life evaluation, and discloses a pure electric vehicle motor fatigue life evaluation method and system, and the method comprises the steps: obtaining historical multi-physical field data and the residual life of a motor in the operation process of the motor of a pure electric vehicle, and carrying out the preprocessing; calculating a synergistic aging influence factor; constructing a data set according to the historical multi-physical field data, the collaborative aging influence factor and the residual life of the motor to train the probability deep learning model, and obtaining a motor fatigue life distribution prediction model; obtaining input data of a target pure electric vehicle, and obtaining a motor fatigue life distribution prediction result through the motor fatigue life distribution prediction model; and based on a Monte Carlo discarding method, sampling the motor fatigue life distribution prediction result for multiple times to obtain a motor fatigue life evaluation result. According to the scheme, accurate evaluation and uncertainty quantification of the fatigue life of the pure electric vehicle motor can be realized, and a decision basis with accuracy and reliability is provided for an active maintenance strategy.

This invention discloses an ultrasonic scanning imaging early warning method and device for regional corrosion, belonging to the technical field of industrial non-destructive testing, aiming to solve the problems of low accuracy in calculating regional corrosion rates and insufficient corrosion trend prediction capabilities. The method includes controlling a two-dimensional scanning mechanism to scan along a preset path, collecting original wall thickness data, location coordinates, and temperature parameters within the monitoring area; compensating for sound velocity based on temperature parameters to correct the original wall thickness data to a compensated wall thickness value; correlating the compensated wall thickness value with the location coordinates to generate a wall thickness distribution heat map of the measured area; calculating the corrosion rate of each measuring point based on the compensated wall thickness values ​​from multiple historical scans of each measuring point and evaluating its data confidence level; and predicting the future trend of the minimum wall thickness in the region and the estimated time to reach the safety threshold. This achieves high-precision calculation of regional corrosion rates, intelligent prediction of corrosion trends, and graded early warning, providing technical support for proactive maintenance of pipelines against corrosion.

System and method for automatic selection of training region selection for deep learning model is disclosed. An identification module 110 identifies equipment shutdowns and excludes buffer region data. A segmentation module 120 estimates operational regions and divides data into predefined operating regions using K-means clustering. A feature analysis module 130 determines top contributing tags and identifies low and high values within operational regions. A domain comparison module 140 compares contributing tags with domain knowledge, excluding regions associated with mapped failures. A data cleaning module 150 ensures data integrity by recalculating statistical measures and eliminating outliers. A filtering module 160 refines data and consolidates regions to reduce training ranges. This integrated approach enhances equipment monitoring and predictive analytics, enabling proactive maintenance strategies and optimizing operational efficiency.

The invention discloses an engineering mechanics experiment data intelligent acquisition and analysis system, and relates to the technical field of engineering mechanics experiments, the system comprises the following components: an equipment perception layer, a digital twin modeling layer, a health degree prediction layer, an active maintenance execution layer and an experiment collaboration layer; according to the method, the fault risk level, the potential fault type and the occurrence probability of the equipment in the future 7-15 days can be accurately predicted through the built-in time sequence prediction model and by utilizing the historical and real-time operation parameters of digital twinborn synchronization, and the prediction capability enables a maintenance team to take measures in advance and implement active maintenance, so that the maintenance efficiency is improved. According to the technical scheme, the system automatically generates a maintenance scheme instead of passively coping with sudden failures, so that the downtime of equipment is remarkably shortened, the experiment efficiency is improved, the maintenance cost is reduced, meanwhile, the maintenance scheme automatically generated by the system comprises detailed maintenance processes, operation specifications and required spare part information, the maintenance processes are further simplified, and the accuracy and efficiency of maintenance work are improved.

This invention discloses an after-sales service management system based on intelligent doors and windows, belonging to the field of door and window service technology. It acquires motor winding temperature rise and shell temperature drop data through a data acquisition module, calculates an acceleration coefficient through a dust accumulation prediction module, and jointly determines the dust accumulation risk. After determining severe dust accumulation and obtaining user authorization, the after-sales maintenance module uploads an evidence package to the cloud, automatically generating and dispatching proactive maintenance work orders. This invention achieves embedded lightweight diagnostics by synchronously acquiring motor winding temperature rise and shell temperature drop data, using low-frequency sampling and integer operations. It also employs a physical consistency logic that combines temperature rise acceleration and temperature drop acceleration to eliminate mechanical overload and environmental interference. This proactively identifies the risk of dust accumulation in the air duct during the user's insidious incubation period, accurately triggering after-sales maintenance and effectively preventing motor burnout accidents.

The invention discloses an equipment state trend prediction and maintenance decision-making system and method for a new energy power station, and the system is deployed based on a cloud-side-end collaborative architecture, and a full-process closed-loop logic of data governance, trend prediction, uncertainty calibration, life evaluation, causal analysis, maintenance optimization, feedback execution, and safety audit is constructed. Accurate sensing, trend pre-judgment and dynamic maintenance decision making of the equipment operation state are achieved. According to the system, multi-source heterogeneous operation data and a physical mechanism model are fused, a cooperative work mechanism of data input-model operation-decision output-feedback iteration is formed by means of core technologies such as space-time diagram prediction, causal reasoning and risk optimization, all the modules are in close linkage with a logic dependency relationship through a standardized data interface, and the reliability of the system is improved. According to the method, the professionality of a single-module function is ensured, the collaboration of the whole scheme is also realized, the active maintenance, risk self-adaption and economical efficiency optimization of new energy power station equipment are finally achieved, and the operation safety and the power generation efficiency of the equipment are remarkably improved.

Techniques for optimizing performance and reliability of a centrifuge are described. In one aspect, operational parameters associated with the centrifuge are obtained to determine a plurality of key performance indicators (KPIs). Each KPI is compared with an expected KPI corresponding to the centrifuge and a deviation in performance of the centrifuge is determined. Based on the deviation determined, the operational parameters, and a plurality of threshold ranges, which include a predicted dynamic threshold range, a fault associated with the centrifuge is determined or a probability of occurrence of an event associated with the centrifuge is predicted, where the event is characterized by a discrepancy in operation of the centrifuge. Further, a performance report with recommended actions to address the fault and the probability of occurrence of an event is generated and issued to personnel of the facility for proactive maintenance.

A system for condition monitoring and predictive maintenance of in-motion components within an industrial system is disclosed. A centralized controller receives image data from vision sensors and operational data from telemetry sensors. An indexing module detects each component at a trigger location, assigns a sequential index during a full system revolution, and associates acquired data with corresponding subcomponents. A measurement module analyzes images to determine characteristics such as wear, deformation, or misalignment, while a predictive model evaluates the measurement data and historical maintenance information to estimate future wear and replacement intervals. An alarm manager compares the measurements to threshold values and generates multi-level condition alarms. The system produces a consolidated report identifying component-specific wear conditions, predicted maintenance timing, and recommended actions. This architecture enables proactive maintenance, reduces downtime, and improves operational reliability in large-scale automated systems by providing continuous or scheduled monitoring and automated assessment of subcomponent health.

The invention belongs to the technical field of industrial equipment intelligent monitoring and health management, and discloses a crane operation state monitoring and structural performance evaluation system based on a digital twinborn technology, which comprises a sensor data acquisition module, a digital twinborn visual platform, a mechanical agent model and a structural performance comprehensive evaluation module. According to the system disclosed by the invention, on the basis of cooperative work of the sensor data acquisition module, the digital twinborn visualization module, the mechanical agent model module and the structural performance comprehensive evaluation module, continuous and online state monitoring and structural performance evaluation of the crane from single operation to a full service cycle are realized; and the lag mode of conventional regular and offline detection is thoroughly changed. According to the system, the operation risk can be pre-warned in a prospective manner, and safety management is promoted to be changed from passive maintenance to active maintenance.

The application discloses a kind of operation and maintenance management systems suitable for coke oven string leakage prevention, belong to coke oven string leakage detection identification field, specifically including operation and maintenance management platform, working condition data acquisition module, data processing analysis module, diagnosis positioning module and hierarchical early warning module;The application is to construct multi-point distribution monitoring network for the key parts of coke oven object, deeply fusion temperature monitoring, pressure monitoring and masonry apparent monitoring, as the front guide early warning of auxiliary gas leakage detection, based on the cross-validation analysis of multi-source data fusion, reach early warning and accurate positioning of string leakage, to solve the hysteresis problem of single gas monitoring, realize the operation and maintenance management system of string leakage prevention of string leakage coke oven with "predictive proactive maintenance" as core.

This invention provides a physical-data hybrid-driven method and system for diagnosing the operational safety of water supply networks. The safety diagnosis method includes steps such as operation and maintenance data collection, extended physical feature generation, data fusion, physical state deduction, hybrid-driven decision-making, and early warning. The system includes modules corresponding to each step. By extending physical features, this invention enables the system to effectively capture the real physical mechanisms of water supply pipeline damage. It utilizes a category-enhancing intelligent algorithm to fuse physical mechanisms with operational data, constructing a physical enhanced feature space, thus achieving accurate and robust prediction of pipeline damage events. Simultaneously, this method quantitatively evaluates the contribution and mechanism of extended physical features and other operation and maintenance data features to the probability of pipeline damage, enhancing the physical interpretability of the model and making the decision-making process more transparent and consistent with domain knowledge, providing a scientific basis for proactive maintenance and asset management of water supply networks.

Systems and methods are directed to increasing the reliability and resilience of power grids, solar farms, and other energy sources, by predicting fault events before they occur and taking preventive action, thus avoiding system down time. In accordance with embodiments, a method of responding to pre-fault events in an electrical system includes monitoring electrical signals generated by the system; comparing the electrical signals to fault signatures; in response to the comparison, triggering a maintenance event, such as dispatching maintenance personnel, automatically disconnecting a component predicted to fail within a pre-determined time limit, or powering down the system, to name only a few examples. The fault signatures can be generated using artificial intelligence.

The invention provides a computer heat dissipation case, and relates to the technical field of computer heat dissipation, a pressurization assembly comprises a pressurization shell, a piston disc and an embedded sleeve, the piston disc and the embedded sleeve are connected to the inner wall of the pressurization shell in a sealed and sliding mode, a plurality of through holes are formed in the embedded sleeve at equal intervals, and the embedded sleeve is located at the upper end of the piston disc; a limiting ring is installed on the inner wall of the pressurizing shell, downward displacement of the piston disc can be limited through the limiting ring, automatic monitoring and active maintenance reminding of the blocking state of the filter screen are achieved, and the core is that a physical signal, namely air inlet resistance increased due to dust accumulation of the filter screen, is transmitted to the pressurizing shell through the limiting ring; force is amplified through a piston disc of the pressurization assembly and converted into elastic potential energy of a spring to be stored, when blockage reaches a preset critical point, the stored energy is released instantly, an impact sleeve of the vibration assembly is driven to impact a plane layer, and clear and audible impact sound and structural vibration are generated; the maintenance mode that a user passively deals with blockage due to the fact that the user cannot perceive the blockage in a traditional mode is thoroughly changed.

The invention relates to the technical field of electromechanical equipment fault diagnosis, and discloses an electromechanical equipment intelligent fault diagnosis system fusing multi-sensor data, which comprises a multi-dimensional monitoring module and an intelligent diagnosis module. According to the system, management data of electromechanical equipment in all areas, operation monitoring data of all electromechanical equipment and environment monitoring data of all areas are obtained through the multi-dimensional monitoring module and are classified to form a data set, and fault causes are comprehensively covered; the intelligent diagnosis module evaluates the creepage risk of each electromechanical device, the grounding state of each area and the wear degree of each electromechanical device, generates corresponding creepage scores, interference scores and wear indexes, dynamically diagnoses creepage faults, ground loop interference and fretting wear problems, is high in multi-dimensional diagnosis precision, is provided with a fixed threshold value, and is high in reliability. And then corresponding diagnosis results and operation and maintenance suggestions are output, so that rapid positioning and active maintenance of faults of the electromechanical equipment are realized, and the dynamic early warning operation and maintenance efficiency is high.

The invention discloses a fire-fighting facility maintenance system based on big data, and relates to the field of fire-fighting facility management. Comprising a data acquisition module which is connected with fire-fighting equipment through a sensing network and is used for acquiring operation parameters and environment data of the fire-fighting equipment; the data transmission module is used for encrypting the acquired data and uploading the encrypted data to a cloud server; the data storage and management module is used for cleaning, classifying and storing the received data, and the big data analysis and early warning module is used for carrying out deep learning and mode recognition on the stored data. All-day real-time monitoring of the state of the fire-fighting equipment, intelligent health diagnosis based on machine learning, fault risk predictive early warning and intelligent closed-loop management of the maintenance process can be achieved, so that passive response is changed into active maintenance, the maintenance efficiency is improved, and it is guaranteed that the equipment is in a good combat readiness state all the time.

The invention discloses a reinforced concrete member function state evaluation method and system and a storage medium, and the method comprises the steps: determining a coefficient corresponding to each influence factor according to the influence degree of each influence factor on the function state of a to-be-evaluated member, and the influence factors comprise material characteristics, environmental conditions, traffic loads and maintenance measures; calculating a dynamic degradation coefficient of the to-be-evaluated component based on the coefficient corresponding to each influence factor; based on the dynamic degradation coefficient, utilizing a component function degradation index model to evaluate the function state of the life cycle; by fusing multiple factors such as environmental deterioration, load damage, material characteristics and active maintenance intervention, functional state evaluation of the bridge reinforced concrete member in the whole life cycle is realized, and by introducing the delay effect of maintenance intervention, the limitation of a traditional linear or exponential model is broken through.

To provide a highly reliable floodproof earth leakage breaker capable of actively warning of potential moisture intrusion before a serious failure occurs. [Solution] The floodproof earth leakage circuit breaker of the present invention houses the earth leakage circuit breaker body (120) within a sealed outer shell (110). Internal humidity sensors (142) and temperature sensors (144) continuously monitor the environment. A microcontroller (150) analyzes this data. In one embodiment, a long-short-term memory (LSTM) model (290) is used to predict seal degradation. If an abnormal or high degradation score is detected, a wireless communication module (160) is used to send a remote alert. This prevents moisture-related failures and enables proactive maintenance, improving the safety and reliability of electrical systems.

A collision detection system is described that integrates machine learning algorithms to enhance the accuracy and reliability of a detection system. In addition, techniques to identify wheel issues, thereby improving the maintenance and operational efficiency of mobile workstations are described. A state machine monitors vibration patterns from the wheels during movement to detect wheel issues, such as a damaged wheel, by analyzing the motion data. Furthermore, techniques to transmit real-time data to an asset management system that may enable proactive maintenance and timely interventions are described.

This invention discloses an AI-based full lifecycle monitoring and management system for boiler equipment in thermal power plants, belonging to the field of intelligent monitoring and management technology for industrial equipment. The system includes a distributed sensing and acquisition module, a data lake and feature engineering module, a multimodal fusion diagnostic engine, a digital twin dynamic health assessment module, and a full lifecycle decision optimization module. This invention aims to solve the problems of difficult multi-source heterogeneous data fusion, delayed fault early warning, and the disconnect between maintenance strategies and equipment status. Through the synergy of the above modules, it achieves deep understanding of boiler equipment status, quantitative health assessment, and proactive maintenance decisions, effectively improving the accuracy of fault early warning and equipment management efficiency.

The invention provides a fault identification and maintenance method, system and equipment for a multistage geothermal well group system, and relates to the technical field of geothermal system maintenance. The method comprises the steps that equipment operation data and equipment loss use data of the geothermal system are collected in real time; determining a current fault point and a current fault type through the equipment operation data and the coarse and fine granularity simulation models; determining a future fault prediction result through the equipment loss usage data; generating a system operation and maintenance strategy according to the equipment loss use data, the current fault point, the current fault type and a future fault prediction result; and performing real-time control on each device through the system operation and maintenance strategy, and pushing the current fault point, the current fault type and the system operation and maintenance strategy to an operation and maintenance object. According to the scheme, cooperative closed-loop control of accurate fault positioning, trend prediction and active maintenance strategy optimization in the multistage geothermal well group system can be realized, the operation stability of the system is improved, and the operation cost is reduced.

The present application provides a method and system for high-speed railway tunnel safety monitoring. The method includes: normalizing the original time series data to construct a multidimensional time series data set; inputting the multidimensional time series data set into a pre-trained LSTM model to predict the structural state of key parts of the tunnel in the future time window, the structural state including the deformation trend of the surrounding rock, the lining stress peak, the water seepage risk level and the crack severity level; triggering a graded warning based on the prediction results of the structural state and the mapping relationship between the preset structural state and the warning level, and generating corresponding tunnel maintenance strategy instructions. The technical solution provided by the present application can realize intelligent perception, accurate prediction and active maintenance decision-making of the tunnel structure state, thereby improving the timeliness and preventiveness of high-speed railway tunnel safety management.

The invention relates to the technical field of predictive maintenance and asset health management of electric power safety tools, in particular to an active maintenance method for an electric power safety tool based on dynamic health prediction. An active maintenance method for an electric power safety tool based on dynamic health prediction comprises the following steps: S1, acquiring process data during use and storage of the electric power safety tool through an RFID tag integrated with a micro sensor, and acquiring a maximum mechanical stress and an accumulated environmental corrosion value according to the process data; and combining the maximum mechanical stress, the accumulated environmental corrosion value and an electronic product code of the RFID tag, adding a cyclic redundancy check code, and packaging into a standard data frame. According to the invention, RFID and automatic detection are fused, a tool total factor data acquisition system is constructed, accurate evaluation of the loss state and prediction of the residual life are realized based on the multi-factor coupling model and the dual-path neural network, and safety management and resource efficiency are improved.