29 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.

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.

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 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.

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 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.

The invention discloses an electrical equipment abnormity prediction method, an electrical equipment diagnosis system and a control device, and relates to the technical field of household appliances, and the electrical equipment abnormity prediction method comprises the steps: obtaining the operation data of electrical equipment; screening out the operation data in a high-risk stage in the operation data to obtain target analysis data; and calling a corresponding exception model based on the target analysis data, determining an occurring exception type, and predicting to-be-maintained zero devices which possibly have exceptions in a plurality of zero devices of the electrical equipment and the exception probability of each to-be-maintained zero device according to the occurring exception type. According to the invention, a traditional passive response mechanism which depends on a user to report a fault is converted into an active maintenance mode which actively discovers all abnormal types before the abnormity appears and realizes preventive maintenance, and the problems that the equipment abnormity cannot be found in time, and early warning and preventive maintenance are difficult to carry out are effectively solved.

This invention belongs to the field of digital twin technology, specifically relating to a digital twin model for turbine component testing. The specific technical solution is as follows: the main model architecture comprises three layers connected sequentially: a physical information network layer, a physical information coupling network layer, and a mapping network layer. The output of the physical information network layer is connected to the physical information coupling network layer, and the output of the physical information coupling network layer is connected to the mapping network layer. This model is used for online operation and maintenance monitoring, fault diagnosis, and health maintenance and management of experimental equipment during the testing process of aero-engine turbine components. It provides early warning of potential damage to turbine hydraulic dynamometer equipment, enabling proactive maintenance, extending the service life of the dynamometer equipment, reducing maintenance costs, and ultimately improving the stability, safety, and economy of the turbine testing process.

This invention provides a method and system for handling loading equipment faults, relating to the field of equipment fault early warning and response technology. By collecting status data during the operation of the loading equipment, extracting fault characteristics, and performing fault identification, this invention can capture early, weak fault symptoms, thus proactively providing early warnings before the fault escalates and promptly addressing early faults, achieving proactive maintenance. Secondly, by selecting a target response level from multiple preset response levels based on the target fault probability, and selecting a target response strategy from multiple preset response strategies based on the target fault type and target response level, this hierarchical fault response mechanism enables the invention to achieve a balance between ensuring production safety and operational efficiency.

The invention discloses a data management method and system for hydropower basin maintenance, and relates to the technical field of data management.The method comprises the steps that total maintenance data analysis of target maintenance equipment is obtained, quality improvement rate deviation is quantified in combination with a benchmark maintenance period, and longitudinal and transverse maintenance quality improvement rate benchmark values are established; the problems that maintenance data are scattered and a unified analysis reference is lacked are solved, and the evaluation accuracy and consistency are improved; target associated equipment is screened and analyzed through work association attributes, the association relationship between the equipment is presented in combination with an association mapping table, collaborative management of the hydropower basin equipment maintenance state is achieved, and cascading failures are avoided; the quality improvement rate of the next maintenance cycle is predicted through the long-short-term memory network model, the predicted maintenance key associated data table is generated and matched with the current data table, key maintenance equipment information is extracted to generate the early warning suggestion form, the problem of maintenance plan lag is solved, prospective judgment is achieved, a basis is provided for active maintenance, and the fault shutdown risk is reduced.

A method and system for early equipment failure detection in cold chain environments that operate independently of direct connections to the monitored equipment. The system features wireless sensor units capturing environmental conditions, such as temperature and humidity, and a remote server analyzing these conditions to predict equipment failures. The server stores location-speci fic records, including operating thresholds and environmental data trends, enabling failure noti fications based on deviations from expected parameters. Noti fications are delivered through user-configurable channels, facilitating proactive maintenance and reducing downtime. By eliminating the need for equipment-speci fic integration, the system provides a cost-ef fective and versatile solution for diverse operations.

The disclosure provides a fault identification and maintenance method and system of a multistage geothermal well group system, and equipment, relating to the technical field of geothermal system maintenance. The method comprises: collecting device operation data and device loss usage data of the geothermal system in real time; determining the current fault point and the current fault type through the device operation data and coarse and fine granularity simulation models; determining the future fault prediction result through the device loss usage data; generating a system operation and maintenance strategy according to the device loss usage data, the current fault point, the current fault type and the future fault prediction result; controlling each device in real time 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 the operation and maintenance object. The scheme can realize the collaborative closed-loop control of accurate positioning, trend prediction and active maintenance strategy optimization of faults in the multistage geothermal well group system, improve the stability of system operation, and reduce operation cost.

The application discloses a wind turbine generator set cabin sliding prediction method, device, equipment and storage medium, the method comprises the following steps: obtaining the operation parameter of the unit in the non-yaw state; the cabin sliding frequency ratio and the proximity switch change frequency ratio are constructed by using the operation parameter; according to two ratios and prediction current fault model, the prediction result of whether the cabin exists sliding fault is obtained; the model construction process is: according to the cabin sliding fault information, the historical operation parameter fault is labeled; the historical operation parameter within the preset time length before the cabin sliding fault corresponding historical operation parameter is labeled as fault; two historical ratios are constructed, and the prediction current fault model is obtained by training. The technical scheme disclosed by the application, by predicting the current fault model construction, the operation parameters within the preset time length before the fault are all labeled as faults, and the two ratios constructed and the prediction current fault model are used to realize early detection of faults in the early stage of faults, so that proactive maintenance protection is realized.

The invention discloses a security equipment active maintenance method and system, a medium and a product, and relates to the technical field of operation and maintenance. The method comprises the following steps: constructing a target digital mirror image twin for target security and protection equipment; generating a multi-dimensional luring strategy library based on the equipment performance, the historical alarm log and the component dependence graph of the target security and protection equipment; when the target security and protection equipment operates normally, activating a logic probe corresponding to the target luring strategy on the target digital mirror image twinborn body, simulating a potential risk state corresponding to the target luring strategy, and monitoring a collaborative probe network corresponding to the target security and protection equipment in real time; when an abnormal feedback signal occurs in the collaborative probe network, analyzing the abnormal feedback signal to obtain an abnormal prediction result; and generating an active maintenance instruction of the target security and protection equipment, and removing the logic probe corresponding to the target luring strategy on the target digital mirror image twin. By implementing the technical scheme provided by the invention, the reliability and safety of the security and protection system can be improved.