238 results about "Fault class" patented technology

The invention discloses an end-to-end fault diagnosis and identification method based on multi-modal fusion, and the method comprises the steps: 1), collecting a vibration signal and an acoustic signal, carrying out the preprocessing, and constructing a training sample set; 2) performing feature extraction to obtain a high-dimensional modal feature vector; 3) generating a sparse adjacency matrix through an end-to-end deep learning graph generation module, and establishing a graph generation structure relation; 4) constructing a multi-receptive field Chebyshev graph convolutional network, and extracting node-level features in a graph generation structure; 5) inputting the structure sensing features into a full-connection layer for mapping, and completing prediction and discrimination of a fault category to which an input sample belongs; performing model supervision training, and optimizing model parameters in an end-to-end mode; and 6) carrying out prediction output on the fault identification model on the test set, and carrying out quantitative evaluation on the fault identification result to obtain the fault identification device.The method belongs to the technical field of equipment operation state monitoring and fault diagnosis, and realizes accurate fault diagnosis of the rotating equipment.

The invention relates to the technical field of power system fault diagnosis, and discloses a distribution network line fault detection data processing method, system and device and a medium, and the method comprises the steps: obtaining load data of a target distribution line detection point, and carrying out the first preprocessing of the load data, and obtaining a fusion feature vector; performing second optimization operation on the fusion feature vector to obtain an optimized fusion feature vector; calculating a potential feature matrix according to the optimized fusion feature vector, and mapping the potential feature matrix to a low-dimensional space to obtain a low-dimensional sample set; presetting an adaptive label propagation algorithm, and performing fault category judgment on the low-dimensional sample set based on the adaptive label propagation algorithm; and storing a judgment result in a relational database. The problems that feature extraction is not accurate in a high-noise environment, and a classification model is insufficient in new fault expansion capacity are effectively solved, and fault signal processing robustness is improved.

The invention discloses an intelligent fault diagnosis method, device and equipment and a storage medium, and relates to the technical field of new energy automobile thermal management, and the method comprises the steps: obtaining a sensor collection parameter, vibration feature information and a heat source coupling parameter; inputting the sensor acquisition parameters, the vibration characteristic information and the heat source coupling parameters into a reinforcement learning model to predict a potential fault, and determining a fault type, a fault confidence coefficient and fault evolution trend information; performing fault identification based on the fault type and the fault confidence, and determining fault type information; and generating a corresponding fault correction report based on the fault category information and the fault evolution trend information. According to the method, the potential fault is predicted in advance through the reinforcement learning model, the type, confidence and evolution trend of the potential fault are accurately deduced, a dynamic fault correction report is generated, the fault is intervened in advance, the diagnosis accuracy is improved, and fault diagnosis from passive response to active prevention and considering reliability and energy efficiency is realized.

The invention discloses an industrial equipment fault detection method, device and equipment based on a large vertical domain model, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining to-be-detected multi-modal data, and carrying out the preprocessing of the to-be-detected multi-modal data into processed data; processing the processed data into a fault category result through a fault detection model; the fault detection model comprises a structure perception enhancement module and a multi-modal fusion module which are connected with each other; the fault detection model is obtained based on training of fault sample data and target pseudo samples, part of the fault sample data is marked with fault label results, and the target pseudo samples are obtained according to the fault label results and the fault sample data by taking obtained field prior data as constraint parameters; the structure perception enhancement module is used for performing feature enhancement processing on the structure data to obtain a structure feature vector; and the multi-modal fusion module is used for performing semantic fusion processing on the structural feature vector, the time sequence data, the image data and the text data. According to the invention, the fault identification precision is improved.

The invention discloses a ship intelligent fault diagnosis method and system based on open label space identification. The method comprises the following steps: obtaining a multi-source sensor time sequence signal of a ship system and constructing a training sample set; carrying out feature extraction on the training sample set by utilizing a deep neural network model, and strengthening the clustering characteristics of the features by adopting a center loss function in the training process; training a classifier on the basis of feature extraction and introducing an open set loss function to form a comprehensive objective function; extracting features from a to-be-diagnosed sample, embedding the features, calculating the distance between the to-be-diagnosed sample and a known fault category feature center, and judging an unknown fault through comparison between the minimum distance and a preset threshold value; and outputting a known fault category or triggering an unknown fault alarm according to a judgment result, and dynamically expanding and updating a model knowledge base based on accumulated unknown fault samples. The method can break through the limitation of the traditional closed set hypothesis, effectively identifies the unknown fault type, and achieves the self-adaptive learning and continuous optimization of a ship fault diagnosis system.

The invention relates to the technical field of artificial intelligence and fault signal diagnosis, in particular to an external gear pump airborne fault diagnosis method and system based on multi-teacher knowledge. According to the invention, on a data set {a pressure signal, a vibration signal and a fault label}, a student module is guided to train through multi-teacher network distillation knowledge, and then the student module is used to predict the fault category of the pressure signal; the multi-teacher network comprises at least two of a physical teacher module, a time sequence teacher module and a space teacher module; in the model training process, the loss function adopts the sum of the loss function of each teacher module and the knowledge distillation loss. According to the method, the problem that in the prior art, airborne rotating part diagnosis faces limitation of computing resources and data sources is solved, and efficient and reliable fault monitoring can be provided under complex working conditions.

The invention provides a fan fault diagnosis method for recognizing a vibration map based on a convolutional neural network, and relates to the technical field of neural networks, and the method comprises the steps: obtaining a multi-dimensional vibration signal in the operation process of a fan, and generating a two-dimensional vibration map through time-frequency transformation; and a convolutional neural network is utilized to automatically extract multi-layer time-frequency features and realize fault category discrimination. In the training stage, parameter optimization is carried out based on known fault samples, in the reasoning stage, real-time signals are input into a trained model to obtain fault type probability distribution, the fault type is determined according to the maximum probability, a fault evolution result is generated in combination with the historical operation trend, and therefore automatic, intelligent and rapid diagnosis of fan faults is achieved.

The invention discloses a rolling bearing fault classification method fusing adaptive distribution perception discrimination loss (ADADL), and belongs to the technical field of rolling bearing fault diagnosis. The rolling bearing fault classification method comprises the following steps of: obtaining a rolling bearing fault, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model. In a complex industrial environment, classification boundary fuzziness is often caused by noise interference and feature overlapping, and the accuracy of rolling bearing fault diagnosis is reduced. According to the method, an adaptive distribution perception discrimination loss function (ADADL) is provided, and intra-class compactness and inter-class separability are improved by adjusting intra-class distance through a dynamic threshold value and optimizing inter-class distribution through an adaptive boundary. And the cross entropy loss is combined with ADADL, so that the classification precision is further optimized, the model is helped to better process samples difficult to classify, and the robustness and the adaptive ability of the model are improved. The classification performance is remarkably improved on the CWRU data set, and particularly, excellent robustness and generalization ability are shown under the conditions of class imbalance and strong noise. Feature visualization results show that ADADL can optimize clustering boundaries of different fault categories, minimize overlapping regions, and relieve the problem of fuzzy classification boundaries.

The invention discloses a motor bearing fault detection system and method based on robust deep learning, and belongs to the technical field of mechanical fault detection and intelligent perception. Feature extraction is carried out on an original vibration signal with a label based on a supervised learning branch network, and the original vibration signal is used as a reference sample; the samples with the same fault category and different fault categories as the reference samples are positive samples and negative samples, and inter-class separation and intra-class aggregation relations in a triple loss optimization embedding feature space are introduced to generate embedding representation; based on an unsupervised learning branch network, encoding the original vibration signal after time domain and frequency domain artificial feature extraction, and introducing triple loss to carry out unsupervised embedding learning to generate high-level feature embedding representation; and the embedded representations output by the two branch networks are fused, dual loss of triple loss and center loss is introduced for training, and a bearing fault detection model after training is completed is used for bearing fault detection.

The invention relates to the technical field of deep transfer learning, and discloses a semi-supervised multi-source-domain generalization fault diagnosis method and system based on mutual information, and the method comprises the steps: obtaining fault vibration signal data of a manual fault test bed bearing, and obtaining labeled source domain data, unlabeled source domain data and unknown target domain data; the method comprises the following steps: extracting deep features of label source domain data and label-free source domain data, classifying the deep features, and generating a preliminary false label and confidence of the label-free source domain data; screening the preliminary pseudo tags based on an adaptive threshold strategy, determining high-confidence pseudo tags, and correcting low-confidence pseudo tags to obtain an updated source domain feature set; and training a double-branch classifier by using the updated source domain feature set, and predicting a fault category label of unknown target domain data. The data of the target domain does not need to participate in training, and the unknown target domain can be diagnosed only by using the source domain data, so that the accuracy of fault diagnosis is improved.

The invention belongs to the technical field of equipment fault diagnosis, and relates to an extra-high voltage converter valve fault diagnosis method and device and a readable storage medium. Inputting a sound signal of the high-voltage converter valve to be detected into a bidirectional gating circulation unit in the fault diagnosis model, and outputting a time feature vector; inputting the time-frequency graph of the sound signal of the high-voltage converter valve to be detected into a plurality of multi-view feature extractors which are connected in series in the fault diagnosis model and have gradually reduced convolution kernel sizes, and obtaining a spatial feature graph based on the output of the last multi-view feature extractor; aligning and fusing the time feature vector and the space feature map, and outputting a target feature vector; and inputting the target feature vector into a classifier in the fault diagnosis model, and outputting the fault category of the to-be-detected high-voltage converter valve. According to the scheme, the problem that noise and fault signals are overlapped in a single dimension is solved, subtle differences of subdivided faults are amplified, and the accuracy and the refinement degree of fault diagnosis of the high-voltage converter valve are improved.

The invention discloses an open set fault diagnosis method combining generative data enhancement and uncertainty measurement, and aims to solve the problems that a traditional closed set fault diagnosis model is high in misjudgment rate and insufficient in generalization when facing unknown fault types. Existing methods generally depend on sufficient samples of known fault categories, and unknown fault modes are difficult to recognize. Therefore, a VAE-GMM generation model based on Gaussian mixture distribution is constructed, an auxiliary negative sample separated from a known category is generated by adopting a low probability density sampling strategy, and the sensitivity of the model to an abnormal sample is optimized in combination with comparative learning. Further, uncertainty quantization is carried out on the prediction result through an EDL classifier, and a threshold judgment criterion is established to distinguish known / unknown fault types. Compared with the prior art, the method has the advantages that through dual-module cooperation of generative data enhancement and uncertainty measurement, the diagnosis robustness in an open set scene is remarkably improved, and meanwhile, the dependence on unknown fault prior data is reduced.

The invention relates to the field of mechanical fault diagnosis, in particular to a fault sample generation method, system and device, a medium and a program product. The method comprises the following steps: setting training set and verification set samples; constructing and training a generative adversarial network model guided by discriminant conditions; the discriminant condition guided generative adversarial network model comprises a generator model and a discriminator model; the generator model obtains a synthetic sample according to the random noise and the category label; and the discriminator model is used for receiving the samples and discriminating and classifying the samples. The discriminator model is provided with two paths of output; the first output is used for judging whether an input sample is true or false; the second output is used for judging whether the input samples are true or false and classifying the input samples; and generating a fault sample by using the trained generator model. According to the method, a high-fidelity conditional generative adversarial network model is constructed, samples of various fault categories can be synthesized at a time, and therefore the problem that the accuracy of an intelligent diagnosis algorithm is low due to scarcity of fault samples in actual engineering is solved.

The invention discloses a bearing continuous fault detection method and system based on a replay enhancement prototype network. The method comprises the following steps: firstly, carrying out preprocessing based on rolling bearing fault data; secondly, constructing a time sequence feature extractor and a category feature modeling system to obtain an initial diagnosis model, and training the initial diagnosis model through a joint loss function for known fault categories based on the preprocessed fault data; and inputting a to-be-detected sample into the trained initial diagnosis model, judging whether the to-be-detected sample is a new category, taking the detected new category as an object, updating the category prototype library based on a prototype extension mechanism, and constructing an incremental training data set to realize incremental iterative optimization. And finally, based on test set data of a new category and a known category, outputting a fault detection result by the incremental optimized REIPN model, and performing performance evaluation. According to the method, the time cost of retraining is remarkably reduced, and the limitation of a traditional fault diagnosis model in the aspect of new category extension is broken through.

The invention belongs to the field of equipment fault diagnosis, and discloses an unsupervised cross-equipment fault diagnosis method fusing dual alignment and pseudo-label learning. Through collaborative optimization of three mechanisms of global domain adaptation, conditional domain confrontation and pseudo-label learning, depth feature alignment and refining of three levels of global-local-instance are realized, and the accuracy, robustness and generalization ability of unsupervised cross-equipment fault diagnosis are significantly improved. The core technical problems of confusion of different fault category features on a target domain and low diagnosis precision due to the fact that an existing domain adaptation method only pays attention to global distribution alignment and ignores a category structure are solved, and the technical bottleneck that when a traditional intelligent diagnosis model is applied to new equipment or new working conditions, the performance can be guaranteed only by depending on label data is solved.

The invention provides a single-source-domain generalization intelligent diagnosis method based on targeted data enhancement, and the method comprises the steps: collecting a state monitoring vibration signal of a key part of industrial equipment under different working conditions, and carrying out the data preprocessing, and the preprocessing comprises signal interception and normalization processing; dividing the state monitoring vibration signals into a source domain sample set and a target domain sample set; constructing a basic fault diagnosis model, wherein the model comprises a targeted enhancement chain, a distributed mixing layer, a feature extractor and a fault classifier; inputting the source domain sample set into the basic fault diagnosis model for model training to obtain a target fault diagnosis model; and inputting the target domain sample set into a target fault diagnosis model, and outputting a fault category of the sample. According to the method, the fault recognition accuracy and robustness under the unknown target working condition are remarkably improved.

The invention relates to a work order generation and processing monitoring method and system, equipment and a storage medium, and the method comprises the following steps: collecting operation data of power equipment, carrying out the preprocessing, then constructing an equipment abnormality prediction model, calculating the occurrence probability of each fault type through the preprocessed data, setting an occurrence probability threshold value, and carrying out the prediction of the abnormality of the equipment; taking the fault type exceeding the threshold as an actual fault type to generate a corresponding work order; work order weights are calculated according to factors such as work order generation time, fault severity and field environment, a work order distribution model is constructed, and reasonable scheduling of maintenance resources is realized; for the completed work order, performing weighted summation through indexes such as response duration and satisfaction, and evaluating the overall processing efficiency; and judging the overall efficiency of each type of fault work order according to a set processing efficiency threshold, and if the overall efficiency does not reach the standard, dynamically adjusting the maintenance resources in the next time period by using the feedback optimization model, and updating the processing efficiency threshold, thereby continuously improving the fault response and maintenance efficiency.

A hydraulic loading system fault diagnosis method based on sequence learning comprises the steps that firstly, sample data and corresponding labels are imported, and the imported sample data are flattened and then subjected to normalization processing; then, a three-layer one-dimensional CNN convolution structure is adopted, each layer comprises convolution, batch normalization, ReLU activation function and maximum pooling operation, and a feature matrix is obtained; then, inputting the feature matrix into a single-layer one-way GRU network to capture a dynamic time sequence dependency relationship, and taking a final hidden state of the dynamic time sequence dependency relationship as a global feature representation; and finally, the global features are mapped to a fault category space by a full connection layer, and a Softmax activation function is used to complete fault classification. Cross entropy is adopted as a loss function, the normalized data sample is used for training, and when the accuracy of the verification set reaches a design value or reaches the maximum training round number, the training is ended; according to the method, the automation degree and reliability of fault diagnosis of the hydraulic loading device are remarkably improved by fusing the advantages of the CNN in feature extraction and the GRU in time sequence modeling.

The invention discloses a two-stage air conditioning system fault diagnosis method and system based on an improved deep residual network, and relates to the technical field of air conditioners. The method comprises the following steps: collecting historical operation data of equipment and parts of the heating ventilation air-conditioning system, and obtaining normal samples and fault samples; after the samples are preprocessed, labels are set for the samples according to categories, and normal-category samples and multi-category fault samples are obtained; constructing a deep residual network model fusing long-tail learning and an attention mechanism, inputting normal samples and various fault samples for training, outputting a predicted value of a data category label and determining a predicted fault type, thereby obtaining a trained heating ventilation air-conditioning system fault diagnosis model; and acquiring actual operation data, inputting the actual operation data into the trained heating ventilation air-conditioning system fault diagnosis model to carry out two-stage fault diagnosis, and outputting a fault type. According to the invention, rapid detection and accurate positioning of the fault of the heating ventilation air-conditioning system in a high imbalance data scene are realized.

The invention discloses a single-phase earth fault comprehensive discrimination method based on multiple fault criteria and weight dynamic calculation, and the method comprises the steps: collecting phase voltage and zero sequence voltage characteristic quantities before and after a power distribution network fault, and discriminating the type of a single-phase earth fault according to the change rule of the characteristic quantities; the method comprises the following diagnosis steps: a, adopting a new zero-sequence current method, calculating the zero-sequence current break variable of each feeder switch based on a zero-sequence current reference value, and achieving the positioning of a fault feeder and a section; b, a new phase current method is adopted, a phase current fault characteristic quantity is defined based on the sum of three-phase current difference values before and after a fault, and positioning of a fault feeder line and a section is achieved; constructing an evaluation model by adopting a fuzzy analytic hierarchy process according to the fault category judged by the parallel diagnosis, and dynamically determining a weight coefficient; and performing weighted fusion on the confidence coefficient according to the weight coefficient to obtain the comprehensive fault probability of each feeder line, selecting the feeder line with the maximum comprehensive fault probability as a fault feeder line, and judging a fault interval.

The invention belongs to the technical field of power supply system monitoring, and particularly relates to a mine wind power supply system monitoring method. In order to solve the problem that the fault category of a training set is different from the fault category of a test set, the invention provides a zero sample learning-based fan fault diagnosis method. Comprising the following steps: S1, building a fan fault diagnosis framework considering unknown faults; s2, embedding a fan fault attribute space in the fault sample space and the fault category space, and establishing a mapping classification of fault samples, fault attributes and fault categories; s3, describing fault attributes to form a fault attribute feature set; s4, performing data preprocessing on the fault attribute feature set by adopting a supervised principal component analysis method, and extracting key fault features; s5, based on the preprocessed fault features, fault classification is carried out; and S6, dynamically updating the diagnosis capability by adopting an incremental diagnosis method.

The invention discloses a federated learning fault diagnosis method and a federated learning fault diagnosis system for multi-source unbalanced data of a harmonic reducer, relates to a harmonic reducer fault diagnosis technology, and aims to solve the problem of low diagnosis accuracy caused by unbalanced sample numbers of different fault categories of the harmonic reducer of an industrial robot and limited single-source signal acquisition information. The method is technically characterized by comprising the following steps of: performing wavelet transform on multi-source signals of different users to construct a time-frequency graph data set; carrying out equalization processing on the unbalanced data set by utilizing an improved data enhancement method; an effective channel attention mechanism is introduced, and the output of a residual branch is weighted through a learnable weight, so that the adaptability of the model to different residual information and the extraction capability of the model to data key features are enhanced; the method comprises the following steps: mining complementary information among multi-source signals through an improved multi-mode variational auto-encoder to perform feature fusion, and constructing a multi-user personalized local model; and the server aggregates local model parameters and updates the model, and guarantees user island privacy data through federal learning, thereby performing fault diagnosis on the harmonic reducer under the multi-source unbalanced data. A harmonic reducer signal acquisition experiment platform is established for verification, the characteristics of multi-source unbalanced data can be effectively extracted by the method, information fusion is realized, the average fault diagnosis accuracy is 98.8%, and the performance is superior to that of the compared method.

This application discloses a data-driven energy dispatch fault diagnosis method, device, and medium, mainly relating to the field of fault diagnosis technology. It addresses the problems of existing solutions neglecting the multimodal distribution characteristics of energy data, failing to effectively capture the complex coupling relationships between the source-grid-load-storage links, lacking awareness of dispatch strategies, and being sensitive to noise interference. The method includes: obtaining a graph convolutional hidden representation matrix and then calculating a sparse dictionary matrix; optimizing the graph convolutional sparse coding objective function, solving for the sparse coding coefficients, and outputting a feature mining data matrix; calculating a dispatch strategy matching degree matrix based on the feature mining data matrix, using introduced dispatch plan data and real-time operational constraints; fusing the operational status classification vector and the dispatch strategy matching degree matrix to obtain predicted fault categories; and iteratively training a diagnostic model based on the predicted fault categories and labeled fault categories until a well-trained diagnostic model is obtained.

The application discloses a kind of under the condition of aviation engine inter-shaft bearing fault diagnosis method of class imbalance based on Cycle-GAN, and its specific include: data acquisition and pre-processing, improvement and establish Cycle-GAN model, train improved Cycle-GAN model and complete signal conversion, complete the fault diagnosis of actual vibration signal by Cycle-GAN model.The application proposes a new fault diagnosis transfer learning method based on Cycle-GAN, by improved Cycle-GAN model, signal sample under known condition is converted into new signal sample under unknown condition, provide the conversion signal of data distribution closer to real signal, while also ensure that conversion signal retains the fault category information in original signal, solve the problem of fault data scarcity in actual scene, and use conversion signal to train classifier, so that the classifier can distinguish fault data under unknown condition.In practical engineering application, especially in the problem of aviation engine inter-shaft bearing fault diagnosis under the condition of class imbalance, the application has wide application prospect.

The application discloses a kind of analog circuit fault diagnosis method based on wavelet scattering and ensemble learning, to solve the problem that analog circuit fault response aliasing leads to difficult fault diagnosis.First, the circuit response is divided into multiple frequency band subsets by wavelet scattering transform, and the fault features of the subsets are enhanced using Fisher discriminant analysis.Second, each frequency band subset is sent to different extreme learning machines under Bagging integration, and the classification accuracy of each fault mode in the frequency band subset is used as the class weight of the extreme learning machine.Then, the output values of each extreme learning machine are weighted to obtain the fused output result, and the fault class is determined accordingly.Finally, two example circuits are simulated, and the simulation results show that the diagnostic accuracy is 100%, indicating that the method is feasible and effective, and can realize fault classification and positioning.

This invention provides a self-diagnostic early warning function abnormal state identification system, relating to the field of thermal power plant safety technology. It includes a data acquisition module for real-time acquisition of raw data; a data processing module for preprocessing the raw data and fault datasets extracted from historical data; a model building module for obtaining an abnormal self-diagnosis prediction model from the union of binary classifiers trained using the processed data; and a state identification module for inputting data into the abnormal self-diagnosis prediction model, identifying fault data, and sending corresponding alarm signals to the operator station based on the output first fault category. By obtaining the abnormal diagnosis prediction model from the union of binary classifiers trained using preprocessed fault data, and determining the corresponding alarm signal based on the fault category output from the real-time data input to the abnormal diagnosis prediction model, the system ensures safe operation and provides a basis for condition-based maintenance.

The embodiment of the specification discloses a photovoltaic module fault detection method, device, computer equipment and storage medium, through target detection on a photovoltaic module image, a preliminary fault detection result is obtained, and in the case that a preliminary fault category included in the preliminary fault detection result belongs to a first easy misjudgment category, image classification is performed on a fault area image included in the photovoltaic module image, a fault classification result corresponding to the fault area image is obtained, and the preliminary fault detection result and the fault classification result are further compared, so that the target fault category is determined based on the comparison result, in this way, through the cooperation of target detection and classification detection, when a fault category misjudged due to the similar image features of the fault area image is identified, the accuracy of fault category identification is improved.

This invention relates to the field of fault diagnosis of mechanical equipment components, and discloses a method, system, medium, and device for fault diagnosis of wet friction components based on MLLM-PINN. The method includes: acquiring three types of basic data (temperature, vibration, and text) of the wet friction component, preprocessing them, and inputting them into an MLLM multimodal feature fusion model to form a high-dimensional fusion feature containing visual, dynamic, and semantic information; constructing a PINN physical constraint model consisting of five fully connected hidden layers using PINN, inputting the high-dimensional fusion feature into the model to output fault category, physical parameter deviation, and damage degree; using a hybrid loss function to perform cross-model collaborative optimization of the MLLM multimodal feature fusion model and the PINN physical constraint model, and optimizing the model parameters to obtain the trained and optimized MLLM multimodal feature fusion model and PINN physical constraint model; and generating a structured diagnostic report for the wet friction component based on the physical prediction results of PINN and the semantic reasoning capability of a large language model.

The invention provides a building air conditioning system fault diagnosis method and system based on universal domain self-adaption, a terminal and a medium, and belongs to the technical field of fault diagnos.The method comprises the steps that a target domain data set and a source domain data set are obtained; constructing a fault diagnosis model, wherein the fault diagnosis model comprises a common feature extractor, a main classifier, a plurality of auxiliary classifiers and an unknown class detector; performing universal domain adaptive training on the fault diagnosis model by using the target domain data set and the source domain data set to obtain a trained fault diagnosis model; and inputting the target domain data set into the trained fault diagnosis model, and processing to obtain a diagnosis result of the target building air conditioning system. Through the combination of the main classifier, the auxiliary classifier and the unknown class detector, the unknown fault class and the known state class can be effectively identified, and the accuracy of the diagnosis result is effectively improved.

The application discloses a kind of power electronic system fault diagnosis methods combined with resampling and integrated learning, belong to power electronic equipment fault diagnosis technical field, the method includes real-time sampling electric power electronic system in the current data of one-half fundamental period, current data is normalized, and normalized data is obtained;According to the normalized data, based on fast fourier transform algorithm, the frequency domain characteristics of normalized data are obtained;According to the frequency domain characteristics, the feature vector of normalized data is obtained by feature extraction selector;According to the feature vector of normalized data, the fault class label is obtained using integrated classification model, and the electric power electronic system fault diagnosis is completed.The application solves the technical problems that the original data sample is unbalanced in the existing fault diagnosis method, which leads to inaccurate diagnosis and even wrong judgment of the existing machine learning model, effectively realizes the diagnosis of various fault types of sensors and power devices of electric power electronic system.