312 results about "Fault class" patented technology

The invention discloses a power equipment fault prediction method based on multi-modal data and related equipment, and belongs to the technical field of intelligent monitoring and fault prediction of power equipment, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of the power equipment; extracting features of the preprocessed multi-modal operation data and performing global feature fusion to obtain multi-modal features; inputting the multi-modal features into a pre-trained fault prediction model to obtain a fault category and an occurrence probability of the power equipment; the fault prediction model is obtained by inputting the multi-modal features into a hybrid neural network for training. Multi-modal operation data of power equipment is collected, key features are extracted by using a deep learning model, multi-modal data fusion is performed, and a fault prediction model is constructed based on a hybrid neural network. Real-time fault early warning and maintenance suggestions are provided, the operation reliability of power equipment is improved, the unplanned shutdown risk is reduced, and the method is suitable for health management of transformer substations, high-voltage power transmission equipment and wind generating sets.

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 discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention discloses a bearing fault diagnosis method based on multi-scale frequency sensing dynamic enhancement, and the method comprises the steps: collecting a vibration signal in a bearing operation state, carrying out the preprocessing of the vibration signal, obtaining a time-frequency matrix, and dividing the time-frequency matrix into a training set and a test set; building a multi-scale frequency network sensing model, inputting a time-frequency matrix in a training set into the model to realize extraction of multi-scale features, then performing pooling, time sequence compression, flattening and dimension reduction on the extracted multi-scale features, and then outputting fault category probability distribution through a classifier; and finally, testing the trained model by using a test set, and calculating evaluation indexes such as accuracy, a confusion matrix, an ROC curve and the like. The method has high accuracy while keeping light weight, breaks through double limitations of fixed frequency band and sensitive rotating speed of a traditional method, and can provide a high-precision and low-cost light-weight solution for engineering application of variable-rotating-speed mechanical fault diagnosis.

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 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 invention discloses a wind turbine generator equipment fault diagnosis method and system based on a large model. The method comprises the following steps: initializing a feature set according to normal data; selecting a plurality of candidate features according to the mixed score of each candidate feature, and storing the candidate features in a feature set; performing sample division on normal data by using a time sequence segmentation algorithm and constructing multi-dimensional spatial-temporal feature representation; constructing a fault diagnosis model: loading a large language model as an infrastructure, and injecting the multi-dimensional spatio-temporal feature representation into an embedded input layer of the large language model; an adaptive pooling layer is accessed after the output of the large language model, and a double-layer MLP classifier is constructed for realizing the identification and classification of different fault types; the first layer of the double-layer MLP classifier compresses an input feature to half of an original feature dimension and applies GELU activation, and the second layer of the double-layer MLP classifier is mapped to a corresponding fault category space; constructing a knowledge mechanism library, providing prior knowledge of the wind turbine generator for the model, designing a loss function driven by the knowledge of the wind turbine generator, and carrying out model training.

The invention discloses an intelligent fault diagnosis method for a thruster of an on-orbit service aircraft, and the method comprises the following steps: building a mathematical model related to the control of the aircraft, and configuring the thruster of the aircraft; making a fault data set, wherein the fault data set comprises the output of a control system under various fault states in the rendezvous and docking process of the aircraft; a liquid neural network is constructed and trained, the liquid neural network comprises an input layer, a liquid layer and a classifier, and the input layer is used for integrating original time sequence data of a control system of the aircraft into a unified input vector; the liquid layer comprises N neurons and is used for carrying out dynamic modeling and time sequence feature extraction on an input sequence; the classifier is used for mapping to a fault category probability through an output layer; the real-time data output by the control system of the aircraft is obtained, the pre-trained liquid neural network is applied to perform fault diagnosis, and the method can perform fault diagnosis in real time.

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 relates to the technical field of unit detection, and discloses an intelligent fault self-diagnosis method for an evaporative condensation integrated screw water chilling unit, which comprises the following steps of: decoupling operation data of a compressor through a blind source separation algorithm, performing image processing on operation data of a condenser in combination with a morphological analysis method, and performing fault diagnosis on the operation data of the condenser; the operation characteristics of the compressor and the operation characteristics of the condenser can be extracted respectively, effective decoupling and clear expression of coupling signals are realized, and by constructing a dual-channel LSTM network comprising a gating fusion unit and introducing a cross-channel coupling gating mechanism, the system reliability is improved. The dynamic coupling relation between the compressor and the condenser can be modeled and regulated in the time dimension, the coupling mechanism between the compressor and the condenser is truly reflected, the time sequence parameter sequence is input into the pre-trained deep LSTM network, the fault types such as control lag and performance degradation can be accurately recognized, and the fault diagnosis accuracy is improved. Therefore, the accuracy and real-time performance of fault diagnosis are improved, and the requirement for intelligent fault management of the water chilling unit is met.

The invention discloses a big data-based machine tool fault diagnosis and analysis method and system. The method comprises the following steps of: acquiring signal data of each part of a machine tool by utilizing a sensor; carrying out de-noising processing on the acquired signal data; the de-noised signals are decomposed by using successive variational mode decomposition, screening is carried out based on a Pearson's correlation coefficient, and the unscreened signals are fused and then are subjected to signal reconstruction with the screened signals; the method comprises the following steps: constructing a fault diagnosis model based on an Adaboost-Reformer integrated algorithm, and optimizing hyper-parameters of the Reformer model by using an improved water circulation optimization algorithm; the reconstructed signals are divided into a training set and a test set, the training set is input into the optimized model for training, machine tool signals collected in real time are input into the trained model after being subjected to denoising and reconstruction processing, and corresponding fault categories are obtained. The method can effectively extract fault features from the multi-mode sensor signals and perform accurate diagnosis, has strong robustness and high efficiency, and is suitable for intelligent fault diagnosis of industrial equipment.

The invention relates to the technical field of fault prediction and computer big data processing, in particular to a generalized zero sample composite fault diagnosis method, device and system based on anti-fact reasoning. According to the generalized zero sample composite fault diagnosis method based on the anti-fact reasoning, a two-stage generalized zero sample composite fault diagnosis model based on the anti-fact reasoning is constructed. According to the model, internal causal components of fault data are pointed out from the angle of causal theory, and then a structural causal model is constructed to describe decoupling and generation of fault features under the guidance of anti-factual reasoning. On the basis, a generative model is improved through a reinforced discriminator in the first stage so as to realize binary classification of a single fault and a composite fault. In the second stage, a single fault category is predicted through supervised training of a classifier, and meanwhile, a traditional zero sample learning method is designed to classify composite faults. According to the method, the diagnosis precision of the model is greatly improved, and the problem of deviation of model diagnosis on visible classes and invisible classes is solved.

The invention discloses a sweeping robot control method and system, and the method comprises the steps: calling a pre-established fault feature library according to a fault type outputted by an abnormality recognition module, and carrying out the matching of corresponding abnormality feature data, and obtaining a preliminary abnormality confirmation result; according to the action recovery instruction, an execution unit of the sweeping robot is driven to adjust the wheel rotating speed or reversely rotate a brush, state feedback data after execution is obtained, and whether the recovery action is completed or not is judged; a new cleaning path scheme is generated according to the condition that the current obstacle avoidance capability is insufficient through a path adjustment demand, and the feasibility of an alternative path is determined; through real-time state data in a task execution process, cleaning efficiency and frequent fault problems are continuously monitored, a feature library is circulated, and operation stability is judged.

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 transmission system gear fault diagnosis method based on incremental learning, and the method comprises the steps: firstly collecting transmission system monitoring data through a sensor, constructing an incremental data set, and dividing the incremental data set into fault diagnosis tasks of different stages; constructing an initial model, and training the model based on the initial fault data; in the test stage, a generalized entropy index is defined to judge whether the fault is a new fault; when new fault type data appears, a small number of samples are randomly selected from the old type data and fused with the new type data to construct a training set, and in order to avoid forgetting of the model to the old type fault data, knowledge distillation is carried out on attention weight and old type prediction output during training; and when the generalized entropy index continues to judge that an unknown fault occurs, repeating the network training step to update the model, and finally realizing incremental learning of a fault mode. According to the method, continuous updating of the fault diagnosis model can be realized for streaming data, the state monitoring capability of the transmission system is improved, and a guarantee is provided for the service safety of a mechanical system.

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 provides a rolling bearing small sample open set fault diagnosis method, and belongs to the field of fault diagnosis. The problems of insufficient fault feature extraction capability and open set threshold strategy stiffness caused by rolling bearing data scarcity in the prior art are solved. The method comprises the following steps: constructing a small sample closed set classification module, wherein the module realizes fault feature extraction and classification by fusing a dense connection network and a prototype network; an open set recognition module is designed, and the open set recognition module constructs a self-adaptive multi-class 3 sigma threshold judgment mechanism based on the self-encoder reconstruction error to distinguish a known fault class and an unknown fault class; a to-be-diagnosed bearing vibration signal is input into the closed set classification module for feature extraction and prototype matching, meanwhile, a reconstruction error is calculated through the open set recognition module and compared with a dynamic threshold value, if the error exceeds the threshold value, it is judged that the fault is an unknown fault category, and otherwise, the closed set module outputs a specific fault category. The method is applied to mechanical part detection.

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 provides a bearing fault diagnosis method and system based on a twin neural network under a small sample, and relates to the technical field of mechanical fault diagnosis and artificial intelligence. The method comprises the following steps: collecting X-axis, Y-axis and Z-axis vibration signals of a bearing, slicing, and generating a time-frequency grey-scale map through short-time Fourier transform to enhance feature expression; the method comprises the following steps: constructing same-class and different-class sample pair training sets, training by adopting a weight-shared twin neural network model which comprises two same sub-networks, and optimizing model parameters by calculating the Euclidean distance of sample pair features and combining a cross entropy loss function; in the test stage, unknown samples are matched based on One-shot and N-shot strategies, and the fault category is judged with the maximum similarity probability. According to the method, the accuracy of 95% or above is achieved under 70 training samples, the noise immunity and generalization ability under the small sample condition are remarkably improved, and the method is suitable for low-cost intelligent diagnosis of industrial equipment.

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 relates to the technical field of insulation switch detection, and provides an extra-high voltage GIS graphene-copper-based composite diversion assembly fault diagnosis method and equipment. Dynamic calibration and anti-interference data acquisition are performed on a target diversion component to obtain a multi-source calibration data set, time-space-frequency domain fusion is performed in combination with a Tucker decomposition mode to obtain a cross-modal coupling feature set, and time-frequency-space domain joint feature extraction and coding are performed on the cross-modal coupling feature set to obtain a joint vector. Fault classification is carried out in combination with an attribute graph attention network model to obtain fault category labels, multi-physics field coupling simulation is carried out according to the fault category labels and a multi-source calibration data set to obtain a fault area coordinate set, and finally residual life prediction is carried out through a dynamic Bayesian network model to obtain residual life probability distribution and a maintenance instruction. According to the invention, through multi-modal data fusion, depth feature extraction, intelligent fault classification, simulation auxiliary diagnosis, life prediction and intelligent operation and maintenance decision, the accuracy of fault detection 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 provides a category recognition model training method for rail transit fault diagnosis and a fault category recognition method, which can be applied to the technical field of rail transit fault diagnosis. The method comprises the steps that a training sample is acquired, the training sample comprises sample fault feature data and a sample fault category label, and the sample fault feature data represents fault feature data collected in a rail transit scene; oversampling processing is carried out on the sample fault feature data to obtain sample synthesis feature data, and the number of the sample synthesis feature data is larger than a preset number threshold value; processing the sample synthesis feature data and the sample fault feature data by using a generative adversarial network to obtain sample target generation data; processing the sample target generation data by using the initial category recognition model to obtain a sample fault category recognition result; and training the initial category recognition model based on the sample fault category recognition result and the sample fault category label to obtain a trained category recognition model.

The invention provides a cabin-type intelligent substation risk early warning method, and relates to the technical field of substation fault diagnosis, and the method comprises the steps: obtaining the historical fault information of a substation; clustering the environment data by adopting a clustering algorithm, and constructing an environment category-fault type-fault probability mapping relation table; obtaining current environment data of the transformer substation, and determining fault categories and fault probabilities corresponding to the fault categories; determining a target device based on the fault category, and determining a monitoring frequency of the target device based on a fault probability corresponding to the fault category and an importance coefficient of the target device; acquiring operation parameters of the target equipment based on the monitoring frequency; comparing the operation parameter with a standard parameter, and determining a parameter variation of the target equipment; taking the parameter variation as the input of a pre-constructed fault diagnosis model, and outputting the fault probability of the target equipment; and when the fault probability is greater than a preset threshold value, generating and sending early warning information. And the system load is reduced.

The invention provides a single-source-domain generalization intelligent identification method based on flow model feature enhancement, and relates to the technical field of mechanical fault diagnosis, and the method comprises the steps: carrying out the interception, length unification and amplitude normalization preprocessing of a collected mechanical vibration time domain signal, converting the signal into frequency domain data through fast Fourier transform, and carrying out the analysis of the frequency domain data; dividing into a single-source domain data set and a target domain data set according to working conditions; constructing a fault diagnosis training model; training a fault diagnosis training model according to a set loss function and an optimization algorithm by using the single-source domain data set to obtain a trained fault diagnosis training model; and constructing a fault recognition model based on the trained fault diagnosis training model, and inputting the target domain data set into the fault recognition model for fault category recognition. According to the method, the problems of high dimension of generated data, interference information and the like of a traditional method are solved, and the model generalization ability and the target working condition fault recognition precision 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.