4486 results about "Fault recognition" patented technology

The invention provides an intelligent subway equipment fault diagnosis method and system assisted by a large language model, and relates to the technical field of data processing, and the method comprises the steps: extracting key information through a large language model, constructing a multi-dimensional equipment fault knowledge graph, obtaining a historical fault data set, and extracting key fault monitoring parameters related to the fault, obtaining a sensing state data set, and judging whether the sensor state data is abnormal or not; and calling a pre-constructed sensing distortion correction algorithm, generating a fault monitoring correction parameter and executing parameter correction, inputting multi-dimensional monitoring data into a diagnosis engine driven by a large language model, and outputting a most possible fault type, cause analysis and recommendation processing strategy and a fault identification report. The technical problems that in the prior art, due to the lack of fusion modeling capacity for the unstructured fault text and the structured monitoring data, the intelligent degree of fault diagnosis is low, and accurate recognition and causal analysis are difficult to achieve are solved, and the fault recognition response speed and accuracy are improved.

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The invention relates to a high-voltage equipment defect detection method based on deep learning and multispectral image fusion, and relates to the technical field of electric power high-voltage equipment state detection. The method comprises the following steps: acquiring an ultraviolet image, an infrared image and a visible light image of the surface of the high-voltage equipment; carrying out image pixel feature-based fusion processing on the ultraviolet image, the infrared image and the visible light image through an image fusion method; establishing a high-voltage equipment defect detection model, and training the high-voltage equipment defect detection model by using the fused image data to obtain a high-voltage equipment defect identification model based on the YOLO-STrans multispectral fusion network; and inputting the ultraviolet image, the infrared image and the visible light image of the outer surface of the power high-voltage equipment into a high-voltage equipment defect identification model to obtain a fault identification result of the to-be-detected power high-voltage equipment. The method can improve the recognition precision of the extremely early insulation degradation and temperature anomaly defects of the surface of the high-voltage power equipment.

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 relates to the technical field of prediction algorithms, in particular to an oil and gas well digital twinborn modeling and prediction analysis system which comprises an underground dynamic data analysis module for extracting variables based on pressure, flow velocity and temperature data, performing data association calculation, analyzing the pressure change rate and the flow velocity trend, calculating the temperature fluctuation range and establishing time period variable analysis; and generating a dynamic parameter mode. According to the invention, through an autoregressive integral moving average model, the dynamic data of each time period is integrated and smoothed, the long-term trend of the data is captured, the subtle change in the production fluctuation is identified, the accurate future yield fluctuation trend is generated, a density clustering algorithm is adopted, key variables are analyzed, and the change mode of abnormal fluctuation is extracted. According to the method, potential fault types are screened, similar data points are classified through the density relation between data, dependence on parameter setting is reduced, abnormity is recognized more accurately under the unsupervised condition, and the fault recognition capability is improved.

The embodiment of the invention provides a new energy automobile charging pile fault monitoring method and system, and relates to the technical field of charging pile fault detection. The new energy automobile charging pile fault monitoring method comprises the following steps: S1, collecting multi-source data in the operation process of a charging pile; s3, preprocessing the multi-source data to generate a multi-dimensional feature data set; s3, constructing a dynamic fault threshold model; s4, on the basis of the multi-dimensional feature data set and the dynamic fault threshold model, multi-level fault detection is executed; s5, establishing a fault prediction mechanism; and S6, generating a fault monitoring report according to the fault type and the fault severity evaluation index. Through multi-source data acquisition, dynamic threshold construction and a multi-level fault detection and prediction mechanism, real-time accurate monitoring and fault early warning of the operation state of the charging pile can be realized, and compared with a traditional method, the accuracy and the advancement of fault identification are remarkably improved.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

The invention relates to an intelligent industrial equipment state monitoring device and method, and the method comprises the steps: installing a vibration sensor, an acoustic sensor and an image sensor at key parts of industrial equipment, and comprehensively collecting the operation data of the equipment. In the data preprocessing stage, vibration and acoustic signals are filtered and denoised, and image data are denoised and enhanced. Extracting multi-dimensional characteristic parameters from three aspects of vibration, acoustics and images; a random forest algorithm is adopted for model training, and the method comprises the steps of data preparation, segmentation, decision tree construction, voting mechanism, majority voting, model verification and optimization, testing and the like. In the model identification stage, the real-time features are input into the trained model, and the equipment state is determined through decision tree prediction and voting summarization. And when an abnormal state is detected, setting a multi-level alarm response according to the probability, and generating a maintenance suggestion. According to the method, the whole-process intelligent monitoring of the industrial equipment is realized, and the fault identification accuracy and the maintenance efficiency are improved.

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

The invention relates to the technical field of photovoltaic power station inspection, and provides a distributed photovoltaic power station unmanned aerial vehicle inspection method and system, and the method comprises the following steps: generating an inspection task list based on the multi-station distribution characteristics of a distributed photovoltaic power station; according to the inspection task list, performing scanning modeling on each photovoltaic sub-power station in sequence through a laser radar, and generating a photovoltaic power station three-dimensional map; dynamically planning an inspection path according to the characteristics of each photovoltaic sub-power station; according to the inspection path, performing multi-angle scanning on each photovoltaic module in different installation environments through a multi-source sensor, and obtaining photovoltaic module image data with position information in combination with the real-time position coordinates of the unmanned aerial vehicle; performing preprocessing, feature extraction and fault identification on the photovoltaic module image data by using a fault identification model, and establishing a fault feature library; and carrying out fusion analysis on the multi-source data, generating a fault detection report of each photovoltaic sub-power station based on the inspection task list, and formulating a corresponding maintenance scheme according to the fault detection report. According to the invention, multi-source data fusion analysis of the photovoltaic module image data, the fault feature library, the position coordinates and the three-dimensional map is realized, and the inspection efficiency and the fault detection accuracy of the distributed photovoltaic power station are improved.

The invention discloses a power equipment intelligent gateway fault detection system based on the Internet of Things, relates to the technical field of power equipment intelligent gateways, and is used for solving the problem that an existing power equipment monitoring system is inaccurate in fault detection. Comprising a power equipment access module, a data preprocessing and edge calculation module, a data storage and analysis module and a fault diagnosis and alarm module which are in signal connection. The power equipment access module is used for collecting operation state data of power equipment; the data preprocessing and edge calculation module performs cleaning, format conversion and preliminary calculation on the collected data, so that data redundancy is reduced, and the processing efficiency is improved; the data storage and analysis module stores the processed data and extracts feature information by using a big data analysis method; and the fault diagnosis and alarm module carries out fault identification based on an analysis result and triggers an alarm signal so as to realize accurate detection and efficient maintenance of the intelligent gateway of the power equipment.

The invention discloses a large model service platform for high-efficiency concurrent computing, particularly relates to the technical field of data analysis, and realizes optimal management of the large model service platform for high-efficiency concurrent computing through a task allocation module, a node health assessment module, a node division module, a dynamic adjustment module and a fault identification module. The task distribution module reasonably distributes computing resources according to task priorities and real-time monitoring data; the node health assessment module accurately assesses node health conditions through preprocessing and feature extraction; the node division module classifies the nodes according to the health assessment result; the dynamic adjustment module dynamically adjusts a resource allocation strategy by using fuzzy logic; the fault recognition module recognizes and repairs fault nodes in time by further analyzing the health conditions of the high-load nodes; the overall performance and the resource utilization rate of the system are improved, efficient and reliable execution of intelligent financial services is ensured, and the stability and the reliability of the system are enhanced.

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

The invention provides a multi-source information fused virtualized network fault rapid positioning method, and relates to the technical field of electric digital data processing, and the method comprises the steps: classifying and sorting historical fault events, constructing a historical dynamic evaluation model, correcting a calibration index, capturing equipment performance changes and abnormities, and enhancing the fault recognition capability. Similar faults are accurately classified and preferentially processed according to severity; in the real-time monitoring stage, real-time multi-source and abnormal data are combined, a real-time dynamic evaluation model and a correction calibration index are calculated, a ratio is applied to calculate a fine adjustment strategy, the real-time performance and accuracy of fault positioning are remarkably improved, and network dynamic changes are quickly responded. And carrying out difference analysis on the adjusted real-time model and the historical model, calibrating a preliminary fault positioning result, and improving the positioning precision and reliability.

The invention discloses an intelligent inspection monitoring method and system based on electronic vision, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: collecting multi-mode inspection data, and carrying out the preprocessing of the data; target identification classification and anomaly detection are carried out based on a convolutional neural network; and optimizing the inspection path by adopting a graph theory model and a shortest path algorithm. According to the intelligent inspection monitoring method based on electronic vision, the image obtained in the inspection process is analyzed through the convolutional neural network, the fault detection precision and the equipment classification accuracy are improved, the flexibility and the accuracy of anomaly detection are greatly improved through the auto-encoder model, and the detection accuracy is improved. The equipment position is regarded as the node of the graph, and the optimal inspection path is calculated by adopting the graph theory model, so that the inspection efficiency is remarkably improved, the inspection scheduling is optimized, the inspection coverage rate is improved, and better effects are achieved in the aspects of fault identification accuracy, inspection efficiency and adaptability.

The invention provides a new energy photovoltaic dynamic inspection method and system based on artificial intelligence, and relates to the technical field of photovoltaic power station intelligent inspection. Inspection is triggered according to weather early warning, performance warning or timed tasks; initial path planning is carried out by combining terrain, weather and historical data, and the path is updated by dynamic obstacle avoidance through an RRT * algorithm; multi-modal data, including visible light images, infrared thermal imaging, EL detection data and positioning data, are acquired during inspection of the unmanned aerial vehicle; the unmanned aerial vehicle data and the ground sensor data are integrated to generate a unified fault feature matrix; positioning a defect area in real time by using a deep neural network, judging a defect type and dividing a fault level; and finally, the health degree of the photovoltaic system is scored according to the fault level, and the safe operation trend is analyzed. The multi-modal data real-time fusion and dynamic path planning are realized, the fault identification precision and the inspection efficiency are improved, the manual inspection cost and risk are reduced, and powerful support is provided for intelligent operation and maintenance of a photovoltaic system.

The invention belongs to the technical field of artificial intelligence, and discloses a computer network fault detection system based on an artificial intelligence technology, which comprises a data acquisition module, a feature extraction and preprocessing module and a fault positioning and repairing module. According to the invention, the detection link is accurate and comprehensive, the data acquisition module performs multi-source fusion to obtain rich materials, the feature extraction and preprocessing module generates multi-dimensional vectors to capture complex features, and multi-level fault identification performs comprehensive troubleshooting and reduces misjudgment; the diagnosis process is intelligent and efficient, the hybrid fault diagnosis model fuses supervised and unsupervised learning, processes known faults and detects unknown anomalies, and supervised learning branches are optimized to improve performance; the repair work is timely and reliable, the automatic repair scheme generation method has strategy library matching, dynamic adjustment and rollback mechanisms, quick response and flexible repair can be achieved, and the subsequent diagnosis accuracy is improved through repair verification multi-dimensional evaluation and closed-loop feedback.

The invention discloses an electromechanical equipment health assessment and early warning method based on multi-mode dynamic perception, and belongs to the field of intelligent operation and maintenance of electromechanical equipment. The problems that in the prior art, a single physical quantity cannot comprehensively reflect the equipment state and a traditional signal processing algorithm cannot adapt to the equipment degradation mode change are solved, a panoramic sensing system covering multiple physical fields such as vibration, temperature and noise is constructed through a multi-mode sensor network and a dynamic weight fusion algorithm, and the multi-physical-field multi-physical-field panoramic sensing method is applied to the multi-physical-field multi-physical-field panoramic sensing system. The problem of isolated island of traditional single-dimensional monitoring information is solved; through a physical-depth mixed feature extraction architecture, combining interpretable engineering features with abstract features extracted by a deep neural network to form a health assessment model with mechanism transparency and mode generalization ability; through deep integration of the digital twin platform and the RPA technology, the manual inspection frequency and workload are reduced, the fault recognition accuracy is promoted to increase year by year, and continuously optimized intelligent operation and maintenance ecology is formed.

The invention relates to a multi-sensor fusion intelligent actuator fault self-diagnosis method and system, and belongs to the technical field of actuator fault diagnosis. The method comprises the following steps: realizing multi-sensor clock synchronization through a unified clock source, generating a synchronous time sequence for asynchronous data such as Hall, pressure and temperature by adopting an interpolation method, and intensively acquiring signals such as rotating speed, torque and vibration; based on a current sensor and temperature data, primary fault identification is carried out through a current overload protection model, and an adaptive processing mechanism is triggered; demodulating the noise-reduced vibration signal, extracting high-frequency energy and detecting the deviation degree between the fault frequency of the bearing and a base line; bearing energy consumption abnormity is detected, and a fusion weight is dynamically set by combining the deviation degree, the energy consumption coefficient and an overload result; and carrying out weighted fusion on the multi-modal features, inputting an intelligent diagnosis model to carry out fault mode identification, and finally outputting a fault type / position and triggering a processing strategy. Accurate and rapid self-diagnosis of the actuator fault is realized.

The present invention relates to the technical field of railway locomotive diesel engine adaptive health diagnosis systems, in particular to a railway locomotive diesel engine adaptive health diagnosis system based on human-in-the-loop. The system performs algorithm classification on time sequence signals collected by sensors deployed on a locomotive diesel engine, and comprises four modules a working condition segmentation module, a deduplication detection module, a model training module and an anomaly detection model module, and two steps an offline training process and an online inference process, so as to complete identification and classification of features of anomaly signals, thereby satisfying the requirements of operation and maintenance engineers for health diagnosis and monitoring of health states of subsystems. In addition, the present invention can associate anomaly signals with original sensor data to conveniently check fault reasons, and for faults with various influence factors and unknown reasons, the accuracy of health diagnosis after model construction is completed can be higher. The present invention has the possibility of the health diagnosis model evolving from a fault identification function to a fault classification function, thereby reserving a direction of development for higher requirements of followup intelligent operation and maintenance.

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 invention discloses an intelligent power plant joint inspection system and method based on video AI and a robot, and the method comprises the steps: dividing an intelligent power plant into a plurality of subareas, evaluating the danger levels of the plurality of subareas according to historical data, and obtaining the danger levels of the plurality of subareas; setting an inspection cycle according to the danger levels of the plurality of partitions, and setting a plurality of robots to acquire operation data according to the inspection cycle to obtain inspection data of the plurality of partitions; acquiring a plurality of monitored videos, and performing video analysis on the acquired monitored videos by using an AI technology to obtain an abnormal position; according to the abnormal position, distributing an inspection robot of a corresponding partition to carry out fixed-point inspection to obtain fixed-point inspection data; and performing fault detection on the periodic inspection data and the fixed-point inspection data by using the fault recognition model, and performing fault early warning according to a fault detection result. The invention relates to the technical field of power plant inspection, and solves the technical problems of unreasonable inspection resource distribution and untimely fault early warning in an existing combined inspection system.

The invention provides a three-dimensional seismic fault identification method based on double-attention multi-scale fusion U-Net. The method comprises the following specific steps: constructing a U-shaped network architecture comprising an encoder, a decoder and jump connection; a constructed multi-scale feature fusion module is embedded in the first level of the encoder, and the extraction capability of fault features of different scales is enhanced through a multi-branch structure; introducing the constructed hole fusion modules into the second and third levels of the encoder, designing and expanding a receptive field by using multiple expansion rates, and capturing fault structures of different scales; a double-attention parallel mechanism is integrated in jump connection, and the sensitivity of channel attention and space attention to fault features is improved; constructing a combined loss function; and finally, performing three-dimensional seismic data training and reasoning based on the optimized model to realize high-precision fault identification. The method has high generalization and accuracy, and especially has good performance in the aspect of seismic image fault identification containing a large fault scale span.

The invention discloses an equipment fault early warning method and device, equipment and a storage medium, relates to the technical field of equipment data processing, and discloses the following steps: obtaining operation data of target industrial equipment and associated data of each operation data; performing exception analysis according to the association degree between the operation data and the associated data to obtain exceptional data; performing graph correlation analysis on the abnormal data according to a preset fault graph to obtain a fault type correlation degree; determining an early warning level and a prediction fault type according to the fault type association degree; performing fault early warning on the target industrial equipment based on the early warning level and the predicted fault type; according to the method, various sensor data of industrial equipment are collected, abnormal data atlas analysis is carried out based on relevance of data when different faults occur in a fault atlas, a root fault reason is recognized, then the relevance degree of different fault types is quantified, the fault types are determined, and the early warning level is determined. The accuracy and efficiency of fault identification are improved, and equipment maintenance is carried out in advance based on potential fault problems.

The invention relates to the technical field of industrial Internet of Things, in particular to a machine equipment online state monitoring and fault diagnosis system, which comprises the following steps of: acquiring multi-source heterogeneous sensing data through an edge computing node deployed on an equipment body, performing adaptive noise filtering and feature dimension reduction processing on original data, and acquiring multi-source heterogeneous sensing data; outputting a standardized equipment state vector set; inputting the equipment state vector set into a dynamic knowledge graph engine, constructing a fault evolution network comprising space-time correlation characteristics based on an equipment operation entropy change quantification model, and generating a graph node connection relationship with a weight coefficient; and inputting the fault evolution network into a migration reinforcement learning module, and outputting a diagnosis decision set comprising a fault type, a severity degree and an evolution path through knowledge migration of a cross-device fault mode. According to the method, the problems of edge redundancy and single feature expression in traditional rule-based atlas construction are effectively avoided, and the structuring ability and physical traceability of fault recognition are improved.

The invention discloses a belt conveyor fault identification and prediction method and system, and relates to the technical field of digital intelligent monitoring of belt conveyors, and the method comprises the steps: collecting the operation data of a belt conveyor in real time through various sensing devices arranged on the belt conveyor, analyzing the collected video data and multi-modal data through an AI algorithm, and carrying out the real-time monitoring of the operation data of the belt conveyor; analyzing the operation state and fault prior information of parts and materials of the conveyor, inputting the information into a dynamic simulation model, simulating the common influence of various faults on the belt conveyor through Monte Carlo simulation, and calculating the probability prediction values of the various faults; finally, an analysis result is displayed in real time in combination with modes of graphic display, probability curves and the like, and working personnel are timely notified of occurred and about-to-occur faults; meanwhile, fault probability prediction and operation data serve as training data again to conduct incremental learning on the AI algorithm, and the accuracy of the AI algorithm is continuously improved.

The invention discloses a power equipment intelligent early warning and maintenance management system based on an AI Agent, and relates to the technical field of power equipment maintenance management, and the system comprises a data preprocessing and weight correction module, a feature extraction and denoising module, a dynamic prediction and fault identification module, a time sequence analysis and intelligent early warning module, and a fault simulation and maintenance optimization module. And the data preprocessing and weight correction module is used for preprocessing the operation data of the power equipment and carrying out weighted analysis on the data of the multi-source sensor by adopting a self-adaptive weight distribution algorithm. According to the method, the hidden deviation problem of the sensor is solved through the multi-source data correction and dynamic weight distribution technology, and key features are accurately extracted and noise is eliminated in combination with a deep learning feature enhancement and denoising method. And a dynamic prediction model and an intelligent early warning generation method are utilized to realize accurate fault prediction and graded early warning, a maintenance strategy is optimized, and the reliability and safety of a power system are improved.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention discloses a rotary machine fault diagnosis method based on residual contraction convolution and an attention mechanism, and belongs to the field of rotary machine fault diagnosis. According to the method, firstly, a soft threshold filtering activation function is designed based on Meta-ACON, a soft threshold filtering module is developed around the activation function, and the module can adaptively learn a filtering threshold and a forward dynamic weighting coefficient, so that irrelevant features are effectively filtered, and the expression ability of effective features is enhanced. And then, in combination with multi-channel and multi-scale residual convolution, a multi-channel residual shrinkage convolution unit is constructed to realize multi-scale and multi-channel effective feature extraction. Besides, according to the method, a high-order statistical modeling idea is utilized, a GSoP attention mechanism is introduced behind a multi-channel residual shrinkage convolution unit, and the ability of extracting discriminative features is enhanced through second-order statistical information of a high-level channel feature map. Test results show that the method has good fault identification capability and generalization capability.