2355 results about "Predictive maintenance" patented technology

The invention provides a transformer life prediction method and system based on machine learning and the Internet of Things, and relates to the technical field of transformer life prediction, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of a transformer, constructing a multi-modal data fusion model based on a graph neural network, and carrying out the prediction of the life of the transformer; and mapping the dimensionality-reduced feature data into a graph structure, inputting transformer multi-modal operation data collected in real time into an optimized fuzzy inference system to obtain a health state evaluation result and a final residual life prediction result of the transformer, and obtaining a prediction result of the residual life of the transformer on the basis of transformer maintenance suggestions and historical maintenance records. Constructing a transformer digital twinborn model for maintenance strategy optimization, inputting the health state evaluation result, the residual life prediction result and the transformer maintenance suggestion into the transformer digital twinborn model, performing fault simulation and predictive maintenance analysis, and based on the fault simulation and predictive maintenance analysis result, obtaining a maintenance strategy of the transformer. And generating an intelligent maintenance scheme in combination with the expert knowledge base.

The invention discloses a predictive maintenance strategy optimization method for transformer area equipment maintenance, relates to the technical field of transformer area equipment maintenance, and is used for solving the problem of poor maintenance effect of transformer equipment. Sensors are installed in a transformer area to collect current, voltage, temperature and other key data, preprocessing, frequency alignment and fusion are carried out to generate the same equipment state data, and when data fluctuation exceeds a threshold value, re-collection and calibration are triggered, a health state model is constructed, and a health index is calculated to evaluate the equipment state. The method comprises the following steps: predicting a future health state through an LSTM model, triggering early warning when the future health state is lower than a baseline, dividing risk levels according to health indexes and risk factors, setting maintenance priorities based on health assessment and fault prediction, applying a multi-objective optimization algorithm in combination with an emergency degree and equipment importance to assess resource requirements, and dynamically monitoring resource use conditions. The prediction precision is improved, and the equipment resource allocation efficiency and the maintenance response speed are improved.

This invention provides a comprehensive maritime shipbroking platform uniting digital twin modeling, AI-driven cargo allocation, blockchain-based contract management, predictive maintenance (AR / VR), route optimization, real-time tracking, and single-window compliance. The digital twin engine continuously simulates vessel performance, enabling data-driven decisions on stowage, scheduling, and maintenance. A cargo-freight matching module optimally allocates shipments, factoring in market rates, vessel metrics, and port congestion. Blockchain-secured smart contracts automate negotiations, ensuring transparency and tamper-proof enforcement. A predictive maintenance system applies advanced analytics to diagnose technical issues early, while the route optimization engine finds cost-effective, emission-compliant routes. Real-time tracking gives stakeholders constant visibility, and the single-window interface integrates Electronic Bill of Lading processes, satisfying IMO and IG P&I standards. Additionally, a dynamic vessel ranking system incorporates SIRE, RightShip, PSC, and user feedback for safer, more efficient chartering decisions. The invention addresses day-to-day operational challenges in maritime logistics, elevating efficiency and compliance.

The invention discloses a photovoltaic module fault diagnosis system and method based on deep learning, which significantly improves the accuracy and comprehensiveness of fault diagnosis through multi-modal data fusion and advanced deep learning technology, and improves the system operation efficiency through the application of an adaptive sampling strategy and edge calculation. The system can simultaneously execute fault classification, severity evaluation and life prediction through a multi-task learning framework, the diagnosis depth and breadth are enhanced, a predictive maintenance decision-making mechanism based on reinforcement learning optimizes a maintenance strategy, the service life of equipment is prolonged, the operation and maintenance cost is reduced, and the maintenance efficiency is improved. The distributed learning framework ensures data privacy and continuous optimization of the model, improves the overall power generation efficiency and economic benefits of the photovoltaic power station, and provides powerful support for large-scale application of renewable energy sources.

The present invention relates to the field of industrial intelligent predictive maintenance, and in particular to an interpretable deep feature fusion network-based industrial intelligent predictive maintenance method, comprising: acquiring gearbox vibration data comprising noise; performing preliminary extraction and noise suppression on features of the acquired data by establishing an interpretable feature extraction module having a physical information constraint; integrating multi-scale features comprising long-distance and local dependencies by means of a dual-branch feature fusion module having global and local feature fusion capabilities; performing dimensionality reduction on a high-dimensional feature and generating an output by means of a classifier to obtain a final fault identification result; and performing interpretability analysis on a diagnosis process of a model. In the present invention, by embedding the signal processing technology having a well-defined physical theory support into a deep neural network, the interpretability and reliability of model inference results are effectively improved while the fault identification accuracy of the model is improved.

The invention provides an equipment state intelligent early warning method based on danger perception, and relates to the technical field of equipment state intelligent early warning, and the method comprises the steps: obtaining equipment operation data which comprises multi-source heterogeneous data; performing feature extraction on the multi-source heterogeneous data to obtain unified feature representation; according to the unified feature representation, constructing a correlation model between the equipment operation state and the feature parameters; and obtaining to-be-analyzed equipment operation data. According to the method, an evaluation index system is automatically generated, an evidence reasoning network is adopted for comprehensive evaluation, online incremental learning and a self-adaptive threshold adjustment mechanism are introduced, real-time and accurate evaluation of the health state of the equipment is achieved, the method effectively fuses multi-source heterogeneous data, expert knowledge and a deep learning technology, and the method is suitable for large-scale popularization and application. The comprehensiveness, accuracy and real-time performance of equipment health state evaluation are improved, and powerful support is provided for equipment predictive maintenance.

The invention relates to the technical field of data processing, and discloses a bridle machine fault monitoring method and system based on the Internet of Things. The method comprises the following steps: collecting multi-source operation data through a sensor; performing filtering segmentation processing on the data to obtain feature data; analyzing the feature data to obtain an abnormal identifier and a fault type; establishing a component association map to position a fault source and predict a development trend; calculating a maintenance priority to generate a maintenance scheme; and recording the actual maintenance result and the prediction deviation to update the fault library and the knowledge parameters. Through multi-source data fusion, intelligent fault diagnosis, predictive maintenance and knowledge self-adaptive updating mechanisms, early recognition and accurate positioning of faults of the bridle machine are realized, the non-planned downtime is effectively shortened, and the equipment reliability and the production efficiency are improved.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention provides an MES-based power distribution cabinet quality inspection analysis and process regulation and control method and system, and the method comprises the steps: converting a quality inspection standard into an executable rule, generating a standardized quality inspection template, evaluating the quality of a single project and a batch, monitoring the mean value and fluctuation of a production process based on a statistical process control theory, and calculating a process capability index. A dynamic threshold value formula is combined to adjust a control limit in real time, process stability is evaluated, equipment data is standardized, production batches are associated, data consistency is verified and ensured, graded early warning is performed according to the deviation degree, a machine learning model is combined to realize predictive maintenance, process parameters are dynamically corrected, historical data are analyzed through machine learning, and the process quality is improved. According to the method, high-order feature quantification influence factors are extracted, abnormal parameter combination is detected in combination with an isolated forest, a process strategy library is updated, closed-loop optimization is formed, full-process automation from data acquisition, real-time analysis and parameter adaptive adjustment to predictive maintenance is realized, and quality inspection efficiency and process stability are improved.

The invention provides an intelligent operation and maintenance method and system for a photo-thermal power generation system based on the Internet of Things, and relates to the technical field of information technologies, and the method comprises the steps: obtaining temperature, humidity and vibration environment parameter data collected by a sensor disposed on power generation equipment; and receiving the environmental parameter data, comparing the environmental parameter data with a preset threshold value, and judging whether the environmental parameters exceed a normal range or not. According to the method, environmental parameters such as temperature, humidity and vibration of the equipment are collected in real time by arranging the sensors, when the parameters exceed the preset threshold value, performance parameter collection is triggered, weighted average calculation is carried out in combination with the environmental parameters, and the equipment health index is obtained; according to the method, intelligent assessment and predictive maintenance of the health state of the power generation equipment are realized, the operation reliability and the maintenance efficiency of the equipment are improved, the equipment fault risk is reduced, and the service life of the equipment is prolonged.

AI-driven predictive maintenance system (100) for industrial plants, including: a multitude of sensors (101) configured to collect real-time operational data from components of the industrial plant, the data including at least one of the following elements: vibration, temperature, pressure and rotational speed; a preprocessing unit (102) configured to filter and clean the operational data to remove noise and inconsistencies; a deep learning engine (103) comprising a neural network model (103a) trained on historical operational data and failure events, wherein the deep learning engine is configured to analyze the real-time operational data and detect anomalies that indicate an impending equipment failure; a prediction module (104) configured to generate risk values ​​based on the detected anomalies, the risk values ​​indicating the probability of equipment failure within a specific time frame; a maintenance optimization unit (105) configured to recommend a maintenance plan based on the generated risk assessments, thereby minimizing downtime and optimizing equipment maintenance; a user interface (106) configured to display the status of the equipment, risk assessments and recommended maintenance measures for the operators.

The invention provides a predictive maintenance management method and system for an industrial robot, and relates to the technical field of equipment maintenance management, and the method comprises the steps: obtaining multi-modal heterogeneous data of the industrial robot, carrying out the intelligent preprocessing, obtaining first preprocessing data, carrying out the cross-modal correlation fusion of the first preprocessing data, generating fusion data, and carrying out the prediction of the industrial robot; extracting complementary redundant information and constructing a semantic feature map; constructing a health state prediction model, adding the semantic feature map into the health state prediction model, mapping the semantic feature map to an embedding space through linear transformation, extracting time sequence information, carrying out feature interaction to obtain interaction features, and carrying out transformation and feature enhancement through a feedforward neural network layer to obtain health state probability distribution; comprehensive health state distribution is generated through Bayesian reasoning fusion, an optimal maintenance strategy is generated in combination with a detection model, an abnormal mode and a key turning point, collaborative maintenance is conducted on the industrial robot, a maintenance result is uploaded to a server, and the current state is stored.

The invention belongs to the technical field of rail transit intelligent monitoring, and particularly relates to an intelligent monitoring method and device for a rail transit air conditioning system. According to the method, the sensor array with the self-adaptive sampling frequency is used for collecting the multi-modal operation data, then the collected multi-modal operation data is preprocessed, the feature degradation track atlas is established, powerful data support is provided for subsequent fault early warning and diagnosis, and the fault diagnosis accuracy is improved. Historical abnormal events are introduced to dynamically correct the health state evaluation base line, the timeliness and accuracy of the evaluation base line are ensured, in comparative analysis of real-time operation data and the dynamic evaluation base line, a health degree scoring and dynamic threshold mechanism is adopted, quantitative evaluation of the health state of the air conditioner system is achieved, and the evaluation accuracy of the health state of the air conditioner system is improved. According to the method, a decision graph containing fault diagnosis and predictive maintenance suggestions is generated by analyzing the propagation path and time sequence relevance of abnormal parameters in the air conditioning system and combining an equipment topological relation graph, so that the troubleshooting and repairing efficiency is improved.

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention discloses a digital twin enabling intelligent pump station preventive operation and maintenance system. Comprising a dynamic twin construction module, a multi-source heterogeneous multi-modal data acquisition module, an edge computing and cloud collaboration module, an equipment health degree evaluation module, a predictive maintenance decision module, a cross-system data fusion module, a self-evolution knowledge graph module, an intelligent diagnosis and early warning module, a self-adaptive maintenance decision module and a man-machine collaboration interaction module. And the dynamic twin construction module comprises a physical-virtual synchronous calibration mechanism and an equipment degradation parameter dynamic updating mechanism. According to the method, the limitation problem of a traditional static model is solved, the method can adapt to nonlinear changes under complex working conditions, the comprehensive judgment and prediction capability of the system on the equipment state can be enhanced, the energy utilization efficiency is improved, the energy consumption is reduced, the decision and verification mechanism is perfected, and the data acquisition and processing problem is improved; the problems of timeliness and flexibility of the model are solved, and the computing architecture and the response capability are optimized.

The invention discloses a power system Internet of Things intelligent detection method, and relates to the technical field of power detection, and the system comprises the following components: S1, data collection and preprocessing, S2, big data analysis and feature extraction, S3, equipment life prediction model construction and training, S4, fault prediction and evaluation, S5, predictive maintenance strategy making, and S6, adaptive adjustment. According to the method, the equipment life prediction model of deep neural network self-learning modeling is adopted, the quantum optimization algorithm is used for optimization, accurate prediction of the residual life and the fault occurrence probability of the power equipment is achieved, and meanwhile, the power equipment state comprehensive evaluation model based on the combination of fuzzy logic and the evidence theory is combined, so that the power equipment state comprehensive evaluation efficiency is improved. The equipment health state grade can be comprehensively evaluated, a scientific basis is provided for formulating a predictive maintenance strategy, a maintenance plan can be reasonably arranged in advance, the maintenance cost is reduced, the service life of equipment is prolonged, and economic benefits and social benefits of a power system are improved.

The invention discloses a large-span bridge body structure health monitoring system based on deformation analysis, and relates to the technical field of large-span bridge body structure health monitoring, the system comprises a multi-modal sensor fusion module, a data processing module, an adaptive algorithm module and a deep learning and predictive maintenance module; and the multi-modal sensor fusion module is used for acquiring data of a plurality of sensors and carrying out cooperative processing. According to the large-span bridge body structure health monitoring system based on deformation analysis, through intelligent fusion of multi-modal sensors, the system can provide more accurate angle measurement under the complex conditions of dynamic loads, environment changes, sensor aging and the like, the measurement precision is improved through sensor redundancy design and a self-adaptive algorithm, and the system is suitable for popularization and application. And meanwhile, the influence of environmental interference and sensor faults on a measurement result is reduced.

The invention relates to the technical field of data processing, and discloses a PTP network performance monitoring method and system based on big data analysis, and a medium. The method comprises the following steps: acquiring PTP message data, equipment state data and RTP stream data; preprocessing the data and calculating a clock error; constructing a time sequence database and an analysis model; real-time monitoring and anomaly detection are executed; performing performance scoring and fault positioning by applying machine learning; and carrying out network optimization and predictive maintenance. According to the PTP network performance monitoring method based on big data analysis, global state monitoring, intelligent anomaly detection, accurate fault positioning and predictive maintenance of the PTP network are achieved, and therefore the high-precision time synchronization requirement of a broadcast and television IP making and broadcasting system is guaranteed.

The invention discloses a power distribution network tower abnormity monitoring method and system, and relates to the technical field of tower monitoring, and the method comprises the steps: arranging a multi-mode sensor on a power distribution network tower, and collecting the state data of the tower; constructing a power distribution network tower digital twinborn model, inputting tower state data, and predicting the future state of the tower; performing regular inspection on the tower by using an unmanned aerial vehicle, and collecting dynamic three-dimensional data of the tower; and the tower state data, the tower future state prediction result and the tower dynamic three-dimensional data are fused, and tower abnormity is monitored and early warning is performed. The invention also discloses a method. Through the application of the multi-source data fusion technology, the accuracy and reliability of anomaly detection are effectively improved, professional guidance is provided for maintenance personnel based on a grading early warning mechanism of a comprehensive anomaly index, the fault processing efficiency is improved, the accuracy and timeliness of tower anomaly monitoring are remarkably improved, and the safety and reliability of the tower are improved. And predictive maintenance and intelligent management are also realized, and the safety and reliability of the power distribution network are greatly enhanced.

The invention relates to the technical field of equipment fault prediction, in particular to a cup labeling equipment fault prediction method and system based on machine learning. According to the method, equipment operation state parameters are converted into a multi-mode pulse sequence with a timestamp synchronization characteristic; loading the purified pulse flow to a quantum bit array for entanglement state evolution, and extracting a three-mode entanglement association tensor; carrying out dimensionality reduction projection on the three-mode correlation tensor to an equipment degradation manifold space, and determining quantum tunneling probability density distribution; constructing a time-varying Hamiltonian of an equipment degradation state based on quantum tunneling probability density distribution, and generating a degradation track cluster according to the time-varying Hamiltonian; and performing time sequence convolution processing on the degradation track cluster, performing probability amplitude amplification on a fault critical point in the track cluster by using an energy level splitting characteristic of a time-varying Hamiltonian, and generating a space-time probability cloud picture. The fault evolution law can be visually presented, the accuracy and timeliness of early fault early warning are improved, and a reliable basis is provided for predictive maintenance.

The invention relates to the technical field of predictive maintenance, in particular to a predictive maintenance method and system for relay protection equipment, and the method comprises the following steps: calculating the change trend of each operation parameter in each time window based on the operation parameters of the relay equipment, and obtaining the change rate of the operation state of the relay equipment; according to the method, the running trend under different time scales is refined, the running state change rate is accurately extracted to quantify the health state of the relay equipment, and the health score attenuation rate is calculated in combination with the equipment aging degree, the contact abrasion degree and the relay action times, so that the health degree decline rate is measured, and accurate judgment of equipment maintenance requirements can be realized; and the maintenance task priority is calculated in combination with the importance weight of the equipment, so that the maintenance resource configuration is more targeted, the load fluctuation condition of the equipment is quantified, and the maintenance period is dynamically adjusted according to the set threshold value to match the load change trend of the equipment, thereby avoiding the problem of excessive maintenance or insufficient maintenance.

The invention provides an intelligent temperature field analysis-based heat dissipation optimization method for an IGBT (Insulated Gate Bipolar Translator) power module of an offshore flexible direct current converter valve. According to the method, an IGBT module space temperature data matrix and multi-point temperature sensor data are collected, and filtering processing is carried out through a multi-scale sliding window and a self-adaptive threshold value; establishing a multi-layer thermal network parameter model, and analyzing temperature dynamic change characteristics; layering the feature data according to the thermal response speed, and performing adaptive mapping and weight calculation; establishing a reinforcement learning model based on the heat dissipation efficiency index set to optimize a heat dissipation strategy; and predicting the temperature field distribution by using the graph structure neural network model. Accurate sensing, dynamic characteristic analysis, self-adaptive control and predictive maintenance of the temperature field of the IGBT module are realized, the heat dissipation efficiency and the temperature uniformity are improved, and the service life of equipment is prolonged.

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

The invention discloses a robot health state assessment method and system based on artificial intelligence. The method comprises the steps of data acquisition and fusion, digital twin health modeling, multi-scale predictive maintenance, fault tolerance control and health state assessment. The invention relates to the technical field of digital robot health analysis, and the method comprises the following steps: constructing an optimized feature set by fusing multi-source sensing data, realizing digital twin health modeling by adopting a dynamic dual-data flow neural differential equation combining physical constraint and wear evolution, and outputting a robot health degree parameter; quantum annealing optimization and a graph attention mechanism are introduced, a multi-level predictive maintenance architecture is constructed, fault propagation prediction and maintenance optimization from a part level to a system level are realized, and fault-tolerant control is realized in combination with model prediction control. And the accuracy, interpretability and practicability of health assessment of the robot are improved.

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

The invention discloses a method for detecting disconnection of a control loop. The method comprises the following steps: acquiring an original signal data set, performing sampling frequency optimization on the original signal data set to obtain an optimized sampling frequency value, performing resampling and noise processing, and generating a quality evaluation index; performing enhancement processing on the preprocessed signal, extracting time-frequency domain features, performing correlation analysis and time sequence decomposition, and generating a reliable feature set and a reliability report; performing feature space mapping and hierarchical feature extraction based on the reliable feature set, executing fault recognition to obtain fault probability distribution, performing credibility evaluation, and generating an anomaly evaluation report and a final diagnosis result; and extracting time sequence characteristics according to the abnormal assessment report and the diagnosis result, performing trend prediction and risk assessment, and generating an optimization decision scheme. According to the invention, through multi-level signal processing and feature analysis and in combination with a credibility evaluation mechanism, accurate detection and predictive maintenance of the control loop broken line fault are realized.

The embodiment of the invention discloses a multi-mode adaptive industrial instrument intelligent control system based on deep learning, and relates to the technical field of industrial instrument intelligent systems. The system comprises a multi-modal data acquisition module used for acquiring multi-modal data of an industrial instrument in real time, the multi-modal data comprising at least two of image, sound, temperature and vibration data; the heterogeneous data fusion module is used for carrying out space-time alignment and feature fusion on the multi-modal data; the deep reinforcement learning control module is used for dynamically optimizing a control strategy and generating a control instruction; the self-adaptive human-computer interaction interface is used for providing visual operation and real-time feedback; and the predictive maintenance subsystem is used for predicting faults and generating maintenance suggestions based on the equipment state. The multi-modal data fusion technology of dynamic weight distribution is adopted, the problem that a traditional single sensor loses efficacy under the complex working condition is solved, and the performance of an industrial instrument in the aspects of measurement precision, self-adaptability and operation and maintenance efficiency is improved.