325 results about "Failure diagnosis" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a fault diagnosis and recovery verification method and device, equipment and a medium. Inputting the candidate event and the context state into an intelligent diagnosis model to obtain a diagnosis result and a fault type, selecting a recovery path in a recovery strategy library based on the diagnosis result, generating a recovery execution instruction, creating a verification experiment based on the instruction, and injecting drill data to obtain verification data, and forming a verification conclusion according to the verification data, associating a recovery execution instruction, and issuing the instruction in the execution environment to complete recovery operation. According to the method, through multi-source monitoring data processing, intelligent diagnosis model reasoning, strategy library path selection, verification experiment verification and execution environment automatic recovery, a full-link automatic process from detection, diagnosis, recovery to verification is constructed, and accurate diagnosis and dynamic recovery in a complex environment are achieved.

The invention provides an equipment health examination method and system based on multi-agent collaboration. The method comprises the following steps: a data acquisition step: acquiring target data to generate a multi-dimensional feature vector; the target data comprises frequency data, process data and control data; an equipment monitoring step: monitoring the equipment based on the generated multi-dimensional feature vector, and outputting a monitoring result; and a deployment processing step: deploying and implementing a corresponding processing scheme according to a monitoring result. The invention provides an equipment early warning and diagnosis method and system based on multi-agent cooperation, and the method comprises the steps: combining the advantages of a professional mechanism model, an AI model and a large language model through the multi-agent cooperation of feature extraction, threshold adjustment, fault diagnosis, decision generation, summary review and closed-loop feedback; the problems that a traditional method is single in model, high in false alarm rate, lagged in decision and the like are solved.

The invention relates to the technical field of medical related information and communication, and discloses a hospital logistics one-stop service inspection and repair platform. The hospital logistics one-stop inspection and repair method comprises the following steps: processing equipment operation data through a multi-modal data fusion network, and generating a state feature vector set; constructing an equipment association graph model by using the space-time diagram attention network in combination with the topological information; detecting the abnormity of a single device and predicting a propagation path through a hierarchical time sequence abnormity detection network, and generating a minimum intervention strategy; an abnormal root cause analysis report is generated by means of a multi-stage interpretability engine, and decision making is supported; routing inspection routes and repair task allocation are optimized based on reports and strategies; according to the invention, integrated management of the whole process from abnormity prediction, inspection planning, fault diagnosis to maintenance dispatching can be realized, the hospital logistics pain point is solved, and the system has significant technical innovation and practical value.

The invention discloses a transformer multi-dimensional state sensing and diagnosing system, which comprises a multi-dimensional state sensing module for acquiring transformer multi-dimensional data in real time through various sensors; the data acquisition and processing module performs processing such as synchronous sampling and filtering on the data and extracts features; the state evaluation and fault diagnosis module evaluates a state and diagnoses a fault by using a three-dimensional evaluation model and a CNN-LSTM fusion model; the self-adaptive early warning module dynamically adjusts a threshold value according to an evaluation result and sends out multi-level early warning; the communication and interaction module realizes data encryption transmission, has a visual interface, and supports interaction diagnosis and remote configuration updating. The system can comprehensively collect data, accurately diagnose faults and efficiently early warn, is convenient to operate and maintain, and improves the operation reliability and the operation and maintenance efficiency of the transformer.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention discloses an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention relates to the technical field of aircraft fault diagnosis, in particular to a vertical take-off and landing aircraft diagnosis generalization enhancement method based on causal weight. Firstly, a directed acyclic graph between flight features and fault types is established through an information flow causal discovery method, the causal relationship between the flight features and the fault types can be clearly presented, and a basis is provided for subsequent analysis. Then, mutual information between nodes is calculated and converted into causal strength for causal enhancement, and expression of the causal relationship in the data is enhanced; in the convolutional neural network, causal weights are used for dynamic weighting to extract local features, causal features of a directed acyclic graph are combined, and feature information of different types is fully utilized. According to the method, features are fused through a graph convolutional network to obtain high-order features, adversarial training is completed through a full-connection network, the constructed fault prediction model integrates various technical advantages, the generalization ability of the model is effectively improved, the model can better adapt to different working conditions and data changes, and aircraft faults are accurately diagnosed.

The invention discloses an aircraft intelligent maintenance and diagnosis system, and relates to the technical field of aircraft maintenance and diagnosis, and the system comprises a data collection module which is used for collecting various data in the actual operation of an aircraft. According to the invention, a digital twin model construction module is utilized to accurately simulate the operation state and fault condition of the aircraft, early discovery and accurate diagnosis of potential faults are realized, the pertinence and effectiveness of maintenance are improved, immersive fault experience and maintenance guidance are provided for maintenance personnel through fusion of VR / AR technologies, and the maintenance efficiency is improved. The maintenance accuracy and efficiency are improved, pre-diagnosis and maintenance scheme verification are carried out on the digital twin model, unnecessary disassembly and detection work is avoided, the maintenance time and labor cost are reduced, potential safety hazards can be eliminated in time through accurate fault diagnosis and an effective maintenance scheme, the flight safety of an aircraft is improved, and the safety of the aircraft is improved. And the maintenance scheme is comprehensively evaluated and optimized, so that the performance and the structural integrity of the maintained aircraft are ensured.

The invention discloses an automatic fault detection and diagnosis processing method and device based on a data platform and a terminal. The method comprises the following steps: collecting system resource data regularly according to a set time interval; analyzing and judging whether the collected system resource data is abnormal or not; when it is judged that the system resource data is abnormal, fault diagnosis is controlled to be carried out, a mode based on combination of preset rule diagnosis, log analysis diagnosis and / or correlation analysis diagnosis is adopted, a fault generation reason is analyzed, and fault generation reason data is generated; and based on the generated fault generation reason data, searching a preset automatic repair strategy corresponding to the generated fault generation reason data, performing fault repair according to a repair scheme recorded by the found automatic repair strategy, and outputting a fault repair result. The invention provides the method capable of automatically detecting the fault, accurately diagnosing the fault reason and automatically executing the repair measure, so that the stable operation of the data platform can be ensured, and the reliability and availability of the data platform can be improved.

The invention relates to the technical field of fault diagnosis, in particular to an electromechanical equipment fault diagnosis method and system based on data analysis. The method comprises the following steps: extracting a historical operation log of a motor, analyzing a current overload state and constructing a current overload spectrogram; then, carrying out insulation layer performance loss gradient analysis based on the spectrogram, and calculating the failure probability of the motor; finally, a risk level is given to the overload current according to the failure probability, an equipment fault diagnosis framework is constructed, and the framework is sent to a control terminal to execute fault diagnosis. According to the invention, the electromechanical equipment fault diagnosis technology is optimized, so that the electromechanical equipment fault diagnosis technology is more accurate.

The invention provides a fault diagnosis method and system for a spindle box of a high-speed gear milling machine based on fault mechanism simulation and data fusion. The method mainly comprises the following steps: S1, calculating a tooth profile wear curve of a gear based on an Archard wear principle; s2, three-dimensional modeling software is used for building system models in different wear states, and simulation data sets under different wear degrees are obtained with the help of dynamics simulation software; s3, establishing a data acquisition system to obtain an experimental vibration data set under the actual working condition of the spindle box; s4, constructing a spindle box fault diagnosis model by using the analogue simulation data set obtained in the step S2; and S5, performing fault diagnosis on the experimental vibration data set obtained in the step S3 by using the spindle box fault diagnosis model constructed in the step S4, and identifying the health condition of the spindle box system. Important data support is provided for a main axle box fault diagnosis and health management system, and the diagnosis accuracy and reliability are improved.

The invention discloses a cable state monitoring and fault diagnosis device and method, equipment and a medium, and the device comprises a data collection module and an intelligent diagnosis unit, and the data collection module collects multi-source data used for diagnosing the state of a cable; the method comprises the following steps: converting multi-source data into a space-time matrix through an intelligent diagnosis unit, carrying out feature extraction, outputting a fault type and a corresponding prediction probability of a cable by adopting a preset multi-label classification and dynamic threshold decision-making mechanism according to the extracted features, and sending out early warning signals of different grades according to the prediction probability. The diagnosis device solves the problems that in the prior art, multi-dimensional data are not fully fused, so that the diagnosis result is not comprehensive and accurate enough; and new fault features caused by cable aging or operation environment change cannot be self-adapted, the model generalization ability is limited, and the model adaptability is poor.

The invention discloses a GMM-L2 fault diagnosis method, device and equipment based on a DBGSA algorithm and a medium, and relates to the technical field of engine fault detection, and the method comprises the steps: obtaining the original data of a wind power engine; processing the original data of the wind power engine to obtain second data; then calculating a first clustering center in the second data for generating a first Gaussian mixture model for calculating a health degree; setting an alarm limit according to the health degree, and if the second data is smaller than the alarm limit, determining that the second data is in an abnormal state; processing the second data in the abnormal state to obtain third data; updating the clustering center of the third data by adopting a DBGSA algorithm of fuzzy clustering to obtain a third clustering center; and setting a membership threshold value of the fault type, and calculating the membership probability of the fault type to which each cluster belongs to obtain the fault type to which the third data belongs. According to the method, missing detection caused by classifying only one data point into one fault can be avoided.

The invention relates to the field of intelligent diagnosis of oil and gas field development, in particular to a fracturing wellhead device failure analysis method and system based on a large model and a knowledge graph. The system is helpful for solving the problem that the accuracy and timeliness of failure analysis and fault diagnosis of the fracturing wellhead device in the prior art are limited. The method comprises the steps of building a knowledge graph, setting a knowledge graph dynamic updating mechanism oriented to the fault evolution process, building an open source large model and introducing a closed loop feedback mechanism. According to the method, the large model, the knowledge graph and the RAG technology are applied to fracturing wellhead failure analysis, and the intelligent level of the oil and gas industry is remarkably improved; the problem that a large model is high in generalization but insufficient in specialty is optimized, and the accuracy of answer generation is improved.

The invention discloses a cascade fault diagnosis method based on a root cause intensity power set belief rule base. According to the technical scheme, the method comprises the steps of 1, constructing a power set belief rule base; the conclusion hypothesis unit can clearly describe a single-point fault state, a composite fault state and a causal chain fault state. And 2, introducing a root cause strength parameter: introducing the root cause strength parameter into the conclusion hypothesis unit constructed in the step 1 for quantifying the causal influence capability of the fault state as the root cause. And step 3, reasoning fusion: calculating the matching degree and the activation degree of each rule, and applying an evidence theory-based reasoning fusion algorithm to an output result to obtain power set confidence distribution of each fault hypothesis. And step 4, fault diagnosis and fault root cause positioning are carried out, and a main fault root cause is output, and the main fault root cause is the root cause with the maximum root cause strength. The method is mainly used for realizing accurate diagnosis of cascade faults in a complex system by popularizing a traditional BRB model to power set reasoning and introducing a root cause strength concept.

The invention relates to a method for diagnosing a flight fault of an aircraft, in which a quick access recorder (QAR) is mounted on the aircraft, in which raw flight data from various sensors of the aircraft are stored, the method comprising the steps of: optimizing the raw flight data, (S1) optimizing the unrelated parameters to obtain optimized data of which the unrelated parameters are removed; performing anomaly detection on the optimized data by using a long short-term memory (LSTM) model to identify an abnormal point (S2); and for the identified abnormal point, determining a fault type by using a decision tree (DT) fault diagnosis model (S3). According to the invention, the reliability and accuracy of flight fault diagnosis can be improved, so that flight accidents are prevented with higher reliability, flight potential safety hazards are eliminated, potential flight risks are reduced, and the maintenance cost is reduced.

The invention discloses a punching machine bearing fault diagnosis method and equipment. The method comprises the following steps: establishing a diagnosis mechanism fusing time-frequency domain feature extraction and multi-branch depth feature modeling; original vibration signals are respectively converted into GADF images to capture time sequence dependence, CWT is adopted to extract transient frequency change, STFT is utilized to obtain frequency spectrum characteristics under a stable working condition, and joint input of a multi-view time-frequency graph is realized. On a model structure, a Swin Transform branch is designed for local space detail modeling, a global perception branch is constructed by combining a CNN module and a GAM module, and multi-category fault classification is completed through self-adaptive pooling and a full connection layer on fusion output. User-defined data loading and a standardized process guarantee the consistency of training data, multi-core maximum mean difference is introduced for feature distribution alignment, and the migration ability of the model under different working conditions and the decoupling recognition ability of composite faults are improved.

The invention discloses a fault diagnosis method and device and a computer readable storage medium. The fault diagnosis method comprises the steps that hardware layer data, system layer data and application layer data are acquired; based on the hardware layer data, the system layer data and the application layer data, constructing fusion feature information; performing fault diagnosis based on the fused feature information to obtain fault diagnosis information; and generating a fault processing strategy based on the fault diagnosis information. According to the method, the multi-dimensional data of the hardware layer, the system layer and the application layer are acquired, the fusion feature information is constructed to perform fault diagnosis and generate the processing strategy, so that the performance of the fault at different levels can be comprehensively captured, the accuracy of positioning the fault reason is improved, missed diagnosis and misdiagnosis are reduced, the comprehensiveness and accuracy of fault diagnosis are improved, and the fault diagnosis efficiency is improved. Meanwhile, a user can conveniently and autonomously solve faults.

The invention discloses a semi-supervised learning-based direct-current charging pile health state evaluation method, and aims to solve the problems that a direct-current charging pile operation state comprehensive evaluation system is incomplete, data dimensions are not comprehensive enough, and data preprocessing is difficult in a large-scale real scene in the prior art. Through a semi-supervised learning method of data preprocessing, feature index design, feature index correlation analysis and hybrid consistency regularization and pseudo label generation, key health indexes are extracted from multi-dimensional data of the DC charging pile, and a comprehensive health state evaluation system is established. And an efficient operation state classification model is trained by using a semi-supervised learning method, so that the accuracy and reliability of fault diagnosis are improved. According to the method, the task load of labeling the equipment by operation and maintenance personnel can be effectively reduced, the operation and maintenance efficiency is improved, and stable operation and safe use of the charging pile are ensured.

The invention discloses a ship propulsion shafting fault diagnosis method based on multi-modal attention fusion, and relates to the technical field of ship fault diagnosis. According to the method, multi-modal data such as vibration parameters, lubricating oil parameters and cooling water parameters of key parts of a ship propulsion shafting are synchronously collected and preprocessed; a modal specific feature extraction strategy is adopted, a multi-modal attention fusion mechanism including intra-modal self-attention, inter-modal cross attention and adaptive dynamic weight distribution is designed, and heterogeneous modal features are effectively integrated. Compared with a single-mode method and a traditional feature splicing method, the method has the advantage that the diagnosis accuracy is remarkably improved. The method has high robustness, and can still maintain high diagnosis accuracy even under the condition that part of sensors fail.

The invention discloses a fault diagnosis method and system for a rotating machine bearing, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring a to-be-diagnosed vibration signal of the rotary mechanical bearing; inputting the to-be-diagnosed vibration signal into a pre-constructed bearing fault diagnosis model, and outputting a fault diagnosis result of the rotating machine bearing; the bearing fault diagnosis model comprises a feature extraction network, a GMM feature enhancement module, a projection distillation module and a dynamic extensible classifier which are connected in sequence. According to the method, catastrophic forgetting can be effectively relieved, continuous diagnosis of newly-occurring accidental faults can be achieved, in the initial task, the model learns and identifies different fault types through a fault bearing data set collected in advance, a basis is provided for the subsequent increment task, the increment task is composed of a plurality of stages, and in each stage, the fault bearing data set is subjected to fault detection. The model enhances playback of old knowledge through historical category pseudo features generated by a Gaussian mixture model, and aligns feature representations of new and old models by using a projection distillation module.

The invention discloses a carrier roller fault diagnosis method based on aliasing period fusion features, and particularly relates to the field of roller fault diagnosis. According to the scheme, the operation data of the carrier roller of the conveyor are collected through the sensor, and the collected original sensing data are preprocessed; the method comprises the following steps: dividing carrier roller sensing data into samples based on sliding window sampling, and dividing into a training set, a verification set and a test set according to 6: 3: 1; a carrier roller fault diagnosis model based on the Times-SCConv network is constructed, and parameters are initialized; training and verifying the Times-SCConv network based on the training set and the verification set, optimizing network parameters by using an optimization algorithm, and storing an optimal weight; and inputting the test set into the optimal Times-SCConv network model to carry out carrier roller fault diagnosis, and finally obtaining a carrier roller fault classification result and test accuracy. According to the technical scheme, the problem that fault feature extraction is difficult when sensing data signals of the carrier roller are subjected to aliasing coupling is solved, and modeling extraction of carrier roller data periodic change features is achieved.

The invention discloses a composite fault diagnosis method for motor overload and pressure abnormity of a wind power water cooling system. The method comprises the following steps: S1, data acquisition; the collected original signal data is preprocessed; s2, constructing a lightweight system performance model, and calculating a pressure residual error; s3, extracting time domain and frequency domain features from the original signal data and the residual signals to form feature vectors; s4, performing fault diagnosis based on a machine learning model, and inputting the feature vector into a trained model; the model outputs a diagnosis result and gives a fault confidence coefficient; and S5, outputting a result, and performing early warning. Predictive maintenance is achieved, early warning can be given out at the early stage of a fault when shutdown or secondary damage is not caused, a maintenance plan can be made in advance, passive first-aid repair is changed into active planning, the non-planned shutdown time is shortened, and the availability of a wind field is improved; the unnecessary on-site inspection frequency is reduced, the excessive dependence on the qualification operation and maintenance experts is reduced, and the troubleshooting time is shortened.

The invention relates to a harvester fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the steps that fault information input by a user is acquired; carrying out vectorization coding on the fault information to obtain a query vector; according to the query vector, a preliminary retrieval result is obtained from a pre-constructed knowledge base, and the preliminary retrieval result comprises at least two fault-related historical cases corresponding to the query vector; and obtaining a fault diagnosis report through a pre-trained retrieval enhancement model according to the at least two historical cases and the query vector. By means of the method, the generated fault diagnosis report is comprehensive in content and clear in structure, explainable basis based on real cases can be provided, the reliability and operability of maintenance suggestions are enhanced, and finally the fault processing efficiency is improved, the misdiagnosis rate is reduced, and the downtime is shortened.

Embodiments of the present disclosure provide a fault diagnosis method and apparatus for service failure, and a storage medium. The method includes: acquiring abnormal information to be diagnosed described in a natural language; retrieving, from a fault knowledge base, similar target known fault knowledge based on the abnormal information to be diagnosed, where the fault knowledge base is used to maintain a plurality of sets of known fault knowledge; constructing a first fault analysis instruction of the abnormal information to be diagnosed according to the target known fault knowledge and the abnormal information to be diagnosed; and invoking a language model to take the first fault analysis instruction as an input, and output a fault analysis result of the abnormal information to be diagnosed.

The invention discloses an emergency hydraulic system and a control method thereof. The emergency hydraulic system comprises a multi-source redundant hydraulic loop, a multi-mode sensing and fault diagnosis module, a cooperative control strategy module, a man-machine cooperative emergency operation interface module and an intelligent energy management module. By adopting the multi-source redundant hydraulic loop design, the problems that a traditional emergency hydraulic system is single in power source and lacks redundancy are effectively solved, and the reliability and stability of the system are remarkably improved. When the main hydraulic pump breaks down, the standby hydraulic pump and the pneumatic auxiliary pump can be rapidly started, continuous operation of the hydraulic system is ensured, and shutdown or safety accidents caused by system faults are prevented. Key parameters of the hydraulic system are monitored in real time, feature extraction and fault recognition are carried out on sensor data in combination with a deep learning algorithm, and rapid positioning and accurate evaluation of faults of the hydraulic system are achieved.

The invention provides a ballast water emergency receiving and processing fault diagnosis method and system based on a knowledge graph, and the method comprises the steps: obtaining the fault performance, real-time operation and medium characteristic information of a ballast water emergency receiving and processing system, constructing a dynamic association link in a pre-configured knowledge association system, the knowledge association system comprises fault performance, causes, component association, medium influence nodes and dynamic association relationships, and is adjusted in real time along with the change of medium characteristics. And performing multipath fault source deduction along the dynamic association link, positioning fault cause nodes, and calling association processing operation node information to generate a fault diagnosis scheme including fault source description, processing flow and medium regulation and control suggestions. And finally, integrating implementation feedback information and fault data, and updating a node association relationship. According to the invention, the accuracy and comprehensiveness of fault diagnosis are improved.

The invention discloses an intelligent medical equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, obtaining the data of medical equipment in an operation process, and constructing an original data set of the medical equipment; s2, preprocessing the original data set of the medical equipment, and extracting an input feature vector of the medical equipment; s3, constructing a multi-task deep neural network model, and combining and constructing an initial result set; s4, on the basis of the initial result set, constructing an overall performance index of the initial result set; s5, based on the overall performance index of the initial result set, a multi-target grey wolf optimization algorithm is adopted to carry out joint optimization on the result, and a final optimization parameter set is obtained; and S6, correcting the result by using the optimization parameter set, and outputting an optimization diagnosis result and an optimization prediction result. According to the method, a joint mechanism of the multi-task deep neural network and the multi-target grey wolf optimization algorithm is adopted, and cooperative output and precision enhancement of medical equipment fault diagnosis and life prediction results are realized.

The invention discloses a new energy vehicle fault diagnosis training system based on digital twinning, and the system comprises a data collection and assimilation module which is used for collecting vehicle operation data, and constructing a digital twinning body; the mechanism map construction module is used for constructing a causal failure mechanism map and generating a failure scene; the sample generation and management module is used for generating a virtual domain sample set, a disturbance domain sample set and a real domain sample set, and combining to form a training data set; the residual fusion diagnosis model module is used for setting a physical residual branch and a disturbance residual branch and outputting a fault diagnosis result; the consistency alignment training module is used for constructing joint loss and optimizing the residual fusion diagnosis model; and the intelligent diagnosis and simulation module is used for executing real-time diagnosis and risk prediction, generating a simulation drilling script and training maintenance personnel. According to the invention, through digital twinning, residual fusion modeling and three-dimensional consistency training, accurate diagnosis and safe and efficient training of new energy vehicle faults are realized.