1184 results about "Diagnostic accuracy" patented technology

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 discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

The invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, and relates to the technical field of fault diagnosis, and the system comprises a data acquisition module, a self-adaptive preprocessing module, a diagnosis engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the self-adaptive preprocessing module carries out preprocessing by adopting self-adaptive variational mode decomposition based on an intelligent optimization algorithm; the diagnosis engine module comprises a multi-physical-quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit, the multi-physical-quantity deep fusion unit performs deep fusion on heterogeneous data through a physical information self-encoder to generate a high-dimensional feature matrix, and the dynamic source number estimation unit adopts a three-layer layered structure to perform online estimation on the number of source signals; an independent component analysis algorithm driven by the running state of the on-line blind source separation unit; and a complete diagnosis process is realized. The problem that a traditional method is low in diagnosis precision under strong noise, multi-source coupling and dynamic working conditions is solved.

The invention relates to an equipment fault mode identification and diagnosis method based on deep learning, and aims to improve the accuracy and generalization ability of equipment fault diagnosis. A vibration signal, a temperature signal, an acoustic signal, a current signal and image data of equipment are synchronously acquired through a multi-modal data acquisition system, and weighted fusion is performed on different modal data by adopting a self-adaptive multi-head attention mechanism. And then, performing time sequence modeling by using a bidirectional long-short term memory network (BiLSTM), and finally outputting a fault type and a fault saliency map to help operation and maintenance personnel to position a fault area. And through a transfer learning technology, the adaptability and diagnosis precision of the model under different equipment and working conditions are further improved. The method can be widely applied to fault diagnosis and intelligent operation and maintenance of various devices, and the operation reliability and the maintenance efficiency of the devices are effectively improved.

The invention belongs to the technical field of computer vision, and relates to a dynamic scene online calibration method and system for three-dimensional target detection. The method comprises the following steps: firstly, acquiring a 3D point cloud around a vehicle, an initial external parameter and a synchronous image, and processing the 3D point cloud, the initial external parameter and the synchronous image by a bimodal static mask generation module to obtain a static region probability graph; calculating a multi-scale residual error of the image and the LiDAR edge image in a static region through a static region fusion module; a historical time sequence modeling module is used to generate time sequence fusion features; performing external parameter correction through a coarse-fine multi-stage external parameter correction module; and finally, according to the predicted external parameters, performing three-dimensional target detection through an image-point cloud bidirectional enhancement module in combination with Transform. Full-scene self-adaptive calibration is achieved, the invalid calibration rate is reduced by 67%, permanent deformation and temporary interference of the sensor can be effectively distinguished, the diagnosis accuracy rate reaches 92%, the false alarm rate is lower than 0.5%, and the three-dimensional target detection performance is improved.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention discloses a multi-modal dynamic fusion and incremental learning fault diagnosis method for deep vertical shaft equipment, which belongs to the technical field of industrial equipment fault diagnosis, and comprises the following four steps of: constructing a pre-training large model to perform feature extraction, and relying on a multi-layer Transformer encoder and a dual loss function, establishing a multi-modal dynamic fusion and incremental learning fault diagnosis model; mining cross-modal universal fault features from vibration, temperature and current multi-modal time sequence data; according to the method, multi-modal features are fused, multi-modal association is constructed, modal weights are dynamically adjusted through a modal gating unit and a time delay compensation attention mechanism to adapt to signal quality changes, and meanwhile time sequence deviation is corrected to achieve accurate association; incremental learning is realized by using a decoupling projection layer, and a lightweight projection module is designed for a newly added fault task to suppress disastrous forgetting; network training is optimized, pre-training loss, incremental learning loss and attention regularization loss are integrated through a multi-objective loss function, and model stability and diagnosis precision are improved. The method has the advantage that the model stability and the diagnosis precision are improved.

The invention provides a new energy power distribution fault diagnosis system and method based on multi-source data fusion, and relates to the technical field of power system fault diagnosis. The system comprises a data processing module, a fusion reasoning module, a graph structure diagnosis module and a control response module. The data processing module performs unified structured processing on the multi-source operation data to generate a feature tensor; the fusion reasoning module constructs a dynamic fusion weight based on the information entropy of each data source, and outputs a fusion confidence judgment result; the graph structure diagnosis module is combined with the distribution network topology to construct a graph structure model, and node-level fault positioning is realized through a graph neural network; and the control response module generates a control instruction based on the diagnosis result, and acquires and executes feedback to update graph structure attributes, thereby realizing a diagnosis closed loop. The method has the advantages of high diagnosis precision, strong adaptability, timely response and the like, and is suitable for intelligent fault identification and dynamic processing in a new energy power distribution scene.

The invention discloses a power distribution equipment remote diagnosis method based on edge computing, and particularly relates to the technical field of intelligent monitoring of power equipment, and the method comprises the steps: an edge computing node collects the operation state data of the power distribution equipment in real time; performing diagnosis analysis locally at the node to generate a preliminary diagnosis result and key data; uploading the structured data to a cloud according to a preset strategy, and checking the integrity; and the cloud platform performs association analysis on the multi-node data to identify common anomalies, dynamically optimizes a diagnosis algorithm, automatically triggers alarms and work order distribution in a grading manner, and realizes rapid closed-loop processing in combination with the positions and skills of operation and maintenance personnel. Through cooperation of the edge and the cloud, communication bandwidth occupation is reduced, diagnosis accuracy and real-time performance are improved, fault response time is shortened, and the method is suitable for line-level monitoring and operation and maintenance management of the power distribution network.

The invention discloses a rolling bearing fault diagnosis method based on a multi-scale residual attention network and an adaptive Transform encoder. The rolling bearing fault diagnosis method comprises the following steps: acquiring original vibration data in the running process of a rolling bearing; segmenting the collected original vibration data into samples with specified lengths, and dividing the samples into a training data set and a test data set; inputting the training data set into a multi-scale residual attention network to perform preliminary multi-scale feature extraction; inputting the feature information extracted by the multi-scale residual attention network into an adaptive Transform encoder to obtain time sequence features; finally obtained feature information is subjected to GAP processing and then is input into a Softmax layer for fault diagnosis; the forward propagation calculation and the back propagation calculation are repeatedly executed to optimize model parameters until the diagnosis accuracy and loss of the training data set reach a stable level; and inputting the test data set into the trained model for fault diagnosis, and determining the health condition of the rolling bearing. According to the method, the adaptability and the diagnosis accuracy in time sequence dependence scenes such as rolling bearing fault diagnosis are enhanced.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention discloses a bearing cross-domain fault diagnosis system and method based on a meta-learning domain adversarial graph convolutional network, and particularly relates to the technical field of mechanical fault diagnosis. Multi-source bearing vibration signals are integrated, and a cross-domain graph structure data set including node features and an adjacent matrix is constructed; performing adversarial training through a feature extractor and a domain classifier of the domain adversarial graph convolutional network, and combining a gradient inversion layer to extract domain invariant features; carrying out internal circulation task adaptation and external circulation element parameter updating by utilizing a element learning framework, and optimizing network parameters; and finally carrying out fault diagnosis on the target domain signal. And the total loss function of the system fuses task classification loss, domain adversarial loss and a graph structure regularization item, so that the cross-domain diagnosis precision is improved. The method effectively solves the problem of model generalization caused by domain difference, is suitable for bearing fault diagnosis scenes with few samples and multiple working conditions, and has the advantages of high robustness and high diagnosis precision.

The invention, which relates to the technical field of medical image processing and intelligent diagnosis, discloses an ERCP image intelligent identification system based on multi-modal fusion, comprising an image acquisition module, an image preprocessing module, a multi-modal fusion model module, a parameter measurement module, an early warning judgment module and a real-time interaction module. The image acquisition module is used for acquiring an ERCP radiography original image in a DICOM format, performing privacy information desensitization processing on the original image, and reserving pixel pitch and catheter size parameters in DICOM metadata; the system has the beneficial effects that privacy protection and parameter retention are realized through the image acquisition module, precise structure segmentation is realized in combination with a multi-modal fusion model, key indexes are automatically quantized by means of the parameter measurement module, intraoperative visualization is enhanced in cooperation with the real-time interaction module, and the accuracy of the system is improved. The problems that traditional ERCP analysis depends on experience and lacks quantitative standards and decision lags are effectively solved, and the method has the remarkable advantages that diagnosis precision is improved, operation time is shortened, and the medical resource utilization rate is optimized.

The invention relates to the technical field of fault diagnosis, in particular to a complex equipment fault diagnosis method and system based on dynamic feature modeling. The method comprises the following steps: acquiring multi-channel sensor data; performing data preprocessing on the acquired multi-channel sensor data; extracting initial channel features from the preprocessed data; on the basis of a direction perception mechanism, performing convolution enhancement after splicing the initial channel features, and generating global context features of structure perception; performing multi-time-sequence scale feature fusion on the global context features based on a multi-scale modulation mechanism; performing health trend guidance on the fused features based on a trend guidance supervision mechanism; the diagnosis precision, the trend perception capability and the practical response efficiency of the system are remarkably improved, the system is effectively adapted to core application scenes of multi-industry equipment in predictive maintenance, online fault perception, remote diagnosis analysis and the like, and the system has good popularization prospects and practical values.

The invention discloses an intelligent diagnosis method for composite faults of a permanent magnet synchronous motor based on digital twinning, relates to the technical field of fault diagnosis, and solves the technical problem of low diagnosis accuracy caused by the fact that fusion of multi-source information is not considered when a twinning model is adopted for fault diagnosis in the prior art. The method comprises the following steps: acquiring prophet data through a physical entity layer and transmitting the prophet data into a twin data layer; virtual current is generated through the virtual model layer and transmitted to the twinborn data layer; the twin data layer performs time-space synchronization on the received data to construct a virtual current signal reference library; the application layer makes a difference between predicted current output by the virtual model layer and current collected by the physical entity layer in real time, whether an electrical fault occurs or not is preliminarily judged in combination with a virtual current signal reference library, and corresponding fault features are input into a classification network according to a judgment result for fault diagnosis; the electromechanical composite fault diagnosis of the permanent magnet synchronous motor is realized by combining a multi-source information fusion technology.

The invention relates to the technical field of equipment fault diagnosis, in particular to an intelligent equipment fault diagnosis system based on a knowledge graph and deep learning, which comprises a data acquisition module, a knowledge graph construction module, a deep learning reasoning module, a diagnosis output module and a feedback optimization module. According to the system, real-time reflection of the equipment operation state is realized through multi-source heterogeneous data acquisition and knowledge graph dynamic modeling, and the fault recognition capability is improved in combination with a double-branch deep learning network and an attention mechanism. A diagnosis result is displayed in a graphical interface, and closed-loop optimization is supported. The device state change can be comprehensively captured, the complex working condition adaptability is enhanced, and the diagnosis accuracy and the intelligent level are improved.

The invention provides a rotating machine fault diagnosis method and system based on an improved deep adversarial migration network. The method and system are suitable for cross-working-condition intelligent diagnosis of rotating machines such as motors, fans and bearings. According to the method, based on frequency domain feature extraction of vibration signals, high-robustness image input is generated through GAF conversion and AUGMIX enhancement; constructing a feature extractor fusing multi-scale convolution and an attention mechanism, and realizing feature migration between a source domain and a target domain in combination with an improved domain adversarial neural network (DANN); and an entropy minimization classifier is introduced to improve the classification confidence of the target domain. And feature extraction and classification performance synchronous optimization are realized through end-to-end joint training, and the classification accuracy and generalization ability are significantly improved under the conditions of sample imbalance and no label target domain. Experimental results show that the method still keeps high diagnosis precision under the conditions of unbalanced samples and cross working conditions, and is suitable for an intelligent maintenance system in an industrial scene.

The invention relates to an aeration fan predictive maintenance method, system and equipment based on multi-modal perception and adaptive learning and a medium. The method comprises the following steps: generating a time sequence data set through synchronous acquisition and combined noise reduction processing of a sensor group; generating a multi-dimensional feature vector through time-frequency feature spectrum characterization and interpretability contribution analysis in combination with dynamic weight distribution coupled by environmental factors; on the basis of the multi-dimensional feature vectors, real-time anomaly detection is carried out at the edge end through a lightweight model, and abnormal data fragments are uploaded to the cloud end; and performing cross-sensor bidirectional reasoning on abnormal data fragments through a reasoning model deployed at the cloud, reconstructing a sensor topological graph, intelligently triggering elastic incremental learning, cooperatively processing equipment degradation trend analysis, multi-source evidence fusion and space calibration, and outputting a life prediction result and a fault thermodynamic diagram. According to the method, the core pain points of high early fault omission ratio, insufficient model robustness and the like are solved, and cost reduction, efficiency improvement and equipment life prolonging are realized while the diagnosis precision is maintained.

The invention relates to the technical field of automatic deployment, in particular to a cloud code deployment system which comprises a resource monitoring module, a task scheduling module, a path construction module, a log analysis module and a container repair module. According to the method, by collecting the node load and dynamically collecting the node load, the bandwidth and the storage capacity, accurate comparison of the resource request and the surplus is achieved, the high-matching-degree task node relation is established, the resource allocation scientificity is improved, the scheduling priority and the mirror image dependency sequence are extracted, and the deployment sequence is optimized in combination with conflict task sorting; the scheduling adaptability in a multi-task environment is enhanced, the port and environment configuration is compared in a path construction link, the deployment compatibility is improved, the dependency relationship between deployment log analysis and fusion timestamp and exception identifier verification is improved, the diagnosis precision is improved, re-deployment is carried out based on state and frequency linkage in an exception processing stage, accurate node positioning is replaced, a closed-loop mechanism is formed, and the reliability of the system is improved. And the system stability and the self-adjusting capability are enhanced.

The invention discloses a thyroid tumor diagnosis method and system based on ultrasonic and cytological image conjoint analysis. The method comprises the following steps: converting a thyroid B ultrasonic image of a patient to be diagnosed into an image feature vector FUS; performing structured feature extraction on the thyroid cytological image of the patient to be diagnosed to obtain a structured feature vector FCYTO; the image feature vector FUS and the structured feature vector FCYTO are converted into a fusion Token sequence; and inputting the Token into a multi-modal feature fusion and prediction network based on a Transform architecture, and carrying out feature fusion and classification prediction so as to obtain the probability that the thyroid tumor of the patient to be diagnosed is malignant. The thyroid tumor diagnosis based on multi-modal fusion is carried out on the basis of ultrasonic and cytological images, so that the diagnosis accuracy is 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 discloses a construction method of a dyskinesia phenotype classification model of a Parkinson's disease patient and a diagnosis system, and belongs to the field of medical auxiliary diagnosis devices. The system comprises an image acquisition module and a phenotype classification module, wherein the classification module is composed of a space-time diagram convolution feature extraction module, a hierarchical node fusion module, a functional brain network construction and feature extraction module, a local brain region feature screening module, a hierarchical brain feature pairing fusion module and an output module. According to the method, multi-modal brain image data are fused, space-time and complex relation characteristics of a brain region are deeply mined, a key lesion brain region is positioned, and dyskinesia phenotypes are accurately classified by training a neural network. The system can significantly improve the diagnosis accuracy of the Parkinson's disease dyskinesia phenotype, has good expansibility and adaptability, and provides a scientific basis for early diagnosis and personalized intervention. The invention further relates to an operation method of the system, an auxiliary diagnosis device and a computer readable storage medium.

A small pressurized water reactor fault diagnosis method based on a CNN-LSTM hybrid network comprises the following steps: firstly, acquiring sensor signals in a pressurized water reactor control system under a normal working condition, introducing corresponding faults at different positions according to characteristics of various typical faults, and acquiring corresponding sensor signals under different working conditions; discrete sampling processing is carried out as a training set and a test set; preprocessing the fault training set by adopting a signal-image conversion method, inputting obtained image data into a CNN-LSTM diagnosis model and training, and then verifying the diagnosis accuracy and robustness of the model based on test set data; parameters in the diagnosis model are continuously adjusted, so that an optimal diagnosis model is obtained; according to the technical means of combining the CNN network for extracting image features and the LSTM for extracting time sequence data fault features, the defects of a traditional fault diagnosis method in the aspects of diagnosis efficiency, accuracy, effect and the like are overcome; the invention further comprises a system, equipment and a storage medium for implementing the method.

The invention relates to the technical field of hidden danger intelligent diagnosis, and discloses a hidden danger intelligent diagnosis method based on multi-modal feature fusion. The method comprises the following steps: firstly, synchronously acquiring a visual image, a voiceprint signal and vibration data of equipment, and constructing a multi-modal original data set; eliminating the time deviation of the multi-source data through space-time alignment processing to obtain a synchronous multi-modal data set; then respectively extracting a feature sequence of each modal, and establishing an association relationship between features by adopting a cross-modal association coding technology; and finally, inputting the fused multi-modal features into a hidden danger discrimination model, and outputting a diagnosis result and a disposal suggestion. According to the method, collaborative analysis of vision, voiceprint and vibration data is innovatively realized, the limitation of traditional single-mode detection is effectively solved, the diagnosis accuracy of equipment composite faults is improved, and a new technical means is provided for intelligent operation and maintenance of industrial equipment.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

The invention discloses a power grid intelligent auxiliary inspection system based on an unmanned aerial vehicle, and relates to the technical field of power grid monitoring. Comprising a risk prediction module, a task planning module, a navigation positioning module, an environment perception module, a data acquisition module, an anomaly detection module, a defect identification module, a sound wave diagnosis module, a monitoring adjustment module, an identification optimization module, a collaborative traceability module and an edge decision module. According to the method, the inspection coverage rate and accuracy can be improved, autonomous risk avoiding and dynamic task rearrangement of the unmanned aerial vehicle are realized, manual intervention and inspection risks are remarkably reduced, the adaptability to different defect types and scenes and the diagnosis accuracy are improved, and the initiative and economical efficiency of power grid operation and maintenance are remarkably improved.

The invention discloses a multi-modal power grid fault diagnosis method and system based on a causal event atlas, and belongs to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: preprocessing historical fault case data of power grid equipment, and constructing a causal event atlas database; when a fault diagnosis request is received, analyzing the fault diagnosis request by the planning agent, generating an initial fault hypothesis set in combination with power field knowledge, endowing a corresponding credibility score to the fault hypothesis, retrieving the cause subgraph as evidence in an iterative loop, and updating the credibility score of the fault hypothesis by the reasoning agent; and generating a multi-modal diagnosis report after the termination condition is met. According to the method, the problems of insufficient causal modeling and poor interpretability of a traditional method are solved, and the diagnosis accuracy, efficiency and user credibility are remarkably improved.

The invention discloses a fault diagnosis method for an aviation hydraulic pump, belongs to the field of aviation hydraulic pumps, and aims to solve the problems of lack of feature information and low diagnosis precision caused by insufficient samples in an existing method. The method comprises the following steps: step 1, constructing a double-source feature extraction module, and respectively extracting time-frequency domain prior features and high-dimensional features of vibration signals; the extracted features are used for calculating a prior feature prototype and a high-dimensional feature prototype of a fault category; step 2, constructing a fault prototype strengthening module, and based on a self-attention fusion mechanism, performing weighted fusion on the prior feature prototype and the high-dimensional feature prototype to generate a fault strengthening prototype; and step 3, constructing a small sample fault classification module, measuring the distance between the test sample and the fault enhancement prototype through cosine similarity, outputting a fault category corresponding to the maximum similarity, and completing fault classification.

The present invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory. The method of the present invention comprises: first, collecting multi-source information of a mechanical device, wherein the multi-source information includes vibration data, image data and sound data; respectively calculating corresponding feature indicators; then, using a PCA algorithm to perform dimensionality reduction processing on the indicators; using a Bayesian fault diagnosis model to perform fault diagnosis, so as to obtain a diagnosis result from each sensor; and then, using the DS evidence theory to perform fault fusion diagnosis. The present invention can comprehensively consider the characteristics of various types of data, extract more effective fault indicators, achieve more comprehensive fault identification, and fuse diagnosis results from a plurality of sensors, thereby ensuring the accuracy of final diagnosis. The present invention is suitable for fault diagnosis of various complex mechanical systems.