57 results about "Proactive maintenance" patented technology

The invention relates to the technical field of data processing, and discloses a method and system for monitoring the running state of reflow soldering equipment. The method comprises the steps of collecting data through a multi-point sensor, extracting temperature gradient, tension fluctuation and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting an early warning threshold value, constructing a fault precursor extraction model to predict a future state, and finally establishing a maintenance strategy optimization system and generating an equipment maintenance plan based on fault early warning information. Through multi-dimensional data fusion and intelligent analysis, early accurate identification of the abnormal state of the equipment, accurate prediction of the fault development trend and active maintenance decision based on quality influence and cost optimization are realized, so that the welding quality stability is improved, the non-planned downtime is shortened, and the maintenance cost is reduced.

The invention relates to the technical field of data analysis, provides an equipment state data management method and system applied to an automatic radix astragali seu hedysari refined production line, and is used for realizing equipment health precise prediction and active maintenance through whole-process data monitoring management. The method comprises the steps that a production line equipment state data set is acquired, a production line equipment state feature set is extracted from the production line equipment state data set, and the production line equipment state feature set comprises multi-dimensional time sequence features reflecting the equipment operation trend and topological features of the incidence relation between the equipment; training an astragalus membranaceus essence production line state prediction model based on the production line equipment state feature set, wherein the astragalus membranaceus essence production line state prediction model is used for predicting the equipment abnormal probability and the performance degradation trend in a selected time period according to the current equipment state feature; and generating an equipment management strategy set according to an output result of the radix astragali seu hedysari refined production line state prediction model, wherein the equipment management strategy set comprises production parameter adjustment instructions and maintenance node planning schemes for different equipment maintenance priorities.

The invention provides a high-speed rail tunnel safety monitoring method and system. The method comprises the following steps: carrying out normalization processing on original time series data to construct a multi-dimensional time series data set; the multi-dimensional time sequence data set is input into a pre-trained LSTM model, the structural state of the tunnel key part in a future time window is predicted, and the structural state comprises the surrounding rock deformation trend, the lining stress peak value, the water seepage risk grade and the crack severity grade; and according to the prediction result of the structure state and a preset mapping relationship between the structure state and the early warning level, triggering hierarchical early warning, and generating a corresponding tunnel maintenance strategy instruction. According to the technical scheme provided by the invention, intelligent sensing, accurate prediction and active maintenance decision making of the tunnel structure state can be realized, and the timeliness and preventability of high-speed rail tunnel safety management are improved.

The invention belongs to the technical field of shield tunneling machine cutterhead center area deformation detection, and discloses a shield tunneling machine cutterhead center area deformation detection system and method. By establishing a correlation model of dynamic load and static deformation, deformation of the center area of the shield tunneling machine cutterhead is comprehensively evaluated, intelligent deformation monitoring of the center area of the shield tunneling machine cutterhead is achieved, the system integrates multi-dimensional parameters such as vibration spectrum, temperature gradient and stress distribution, a prediction algorithm is established in combination with material fatigue characteristics, and the prediction accuracy is improved. The deformation condition monitoring recognition rate is improved, and the construction safety and the equipment reliability are remarkably improved. According to the method, the deformation area and the risk area are identified based on the correlation model of the dynamic load and the static deformation, the deformation area can be accurately identified, the risk level can be divided, early risk prediction and active maintenance are realized, the limitation that only the deformed state is detected traditionally is broken through, and the perspectiveness and reliability of health management of the cutterhead are remarkably improved.

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 discloses a safety management system and method for automatic control of a stacking machine, and relates to the technical field of automatic control. Through vertical layered monitoring and multi-dimensional vibration data analysis, a goods shelf is divided into independent monitoring areas according to layer numbers, and the vibration frequency and amplitude of the stacking machine on the same layer are integrated for transverse comparison; structural stress change trends at different heights are accurately identified, and misjudgment caused by single-dimension data is avoided. Historical maintenance data and a real-time vibration prediction model are integrated, a time sequence prediction model is combined to dynamically evaluate the future vibration trend of equipment, a maintenance risk value and a vibration risk value are comprehensively calculated, a traditional lagging maintenance mode is broken through, and data support is provided for active maintenance decision making. Potential risk equipment clusters are identified by using a cosine similarity matching technology, hidden dangers of similar equipment are positioned in advance in combination with a prediction and early warning mechanism, upgrading from single-machine risk management and control to cluster risk monitoring is realized, and the accuracy and response speed of maintenance resource allocation are remarkably improved.

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.

This application relates to the field of data processing technology and discloses a method and system for monitoring the operating status of reflow soldering equipment. This method includes: collecting data through multi-point sensors, extracting temperature gradients, tension fluctuations, and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting a warning threshold, constructing a fault precursor extraction model to predict future states, and finally establishing a maintenance strategy optimization system based on the fault warning information to generate an equipment maintenance plan. Through multi-dimensional data fusion and intelligent analysis, this application achieves early and accurate identification of equipment abnormalities, accurate prediction of fault development trends, and proactive maintenance decisions based on quality impact and cost optimization, thereby improving welding quality stability, reducing unplanned downtime, and lowering maintenance costs.

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 discloses an operation evaluation method, system and equipment of a power transformation equipment line and a storage medium. The method comprises the following steps: constructing a three-dimensional model of a digital twin of power transformation equipment by combining laser scanning and photogrammetry; constructing a virtual sensor model, inputting historical data into a virtual sensor, and carrying out preprocessing and feature engineering output equipment state comprehensive scores; constructing a comprehensive risk assessment function based on the equipment state comprehensive score, and triggering an alarm when the assessment function exceeds a preset safety range; combining the three-dimensional modeling with a comprehensive risk assessment function to construct a defect state model, and inputting a combined result into the defect state model to identify an apparent defect and an overheating area; collecting real-time data, processing the real-time data, and inputting a defect state model to predict the future state of the equipment; and early warning and maintenance decision making. According to the method, the environment temperature and the environment humidity are considered, the equipment state can be comprehensively evaluated, in addition, potential equipment faults can be warned in advance, and an active maintenance strategy based on data driving is provided.

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 present invention belongs to the technical field of deformation detection in the center area of the cutterhead of a shield machine, and discloses a system and method for detecting deformation in the center area of the cutterhead of a shield machine. The present invention realizes intelligent deformation monitoring of the center area of the cutterhead of a shield machine by establishing a correlation model between dynamic load and static deformation, comprehensively evaluating the deformation in the center area of the cutterhead of a shield machine, and integrating multi-dimensional parameters such as vibration spectrum, temperature gradient, and stress distribution, and establishing a prediction algorithm in combination with material fatigue characteristics, thereby improving the deformation monitoring recognition rate and significantly improving construction safety and equipment reliability. The present invention identifies deformation areas and risk areas based on a correlation model between dynamic load and static deformation, and can accurately identify deformation areas and divide risk levels, realize early risk prediction and proactive maintenance, break through the limitations of traditional methods of only detecting deformed states, and significantly improve the foresight and reliability of cutterhead health management.

The present invention provides an engine condition-based maintenance method based on flight parameter data and vibration data, including calculating the kurtosis value of the flight parameter data to perform anomaly detection on the flight parameter data; establishing a vibration anomaly detection model based on the vibration data to perform fault prediction on the vibration data; establishing a confidence hypothesis model, using the anomaly detection result and the fault prediction result as input vectors, outputting the engine health state result, and giving the prediction probability of the engine condition-based maintenance. The method designed by the present invention effectively solves problems such as low data utilization rate, few statistical analysis flights, and unclear support for maintenance guarantee, can provide strong support for changing the engine from passive maintenance guarantee to active maintenance guarantee, helps improve the aircraft attendance rate and maintenance efficiency, and provides support for scientific and reasonable inspection and maintenance.

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.