386 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 belongs to the technical field of computer application, and relates to an operation inspection equipment fault detection method and system based on an improved deep learning model, and the method comprises the steps: obtaining original data, employing EEMD to decompose the original data for feature enhancement, obtaining a decomposition signal, carrying out the noise reduction of the decomposition signal, and obtaining a feature signal after noise reduction; according to the feature signals after noise reduction, a deep learning model is constructed based on Resnet and a dynamic Transform encoder, and the prediction probability of each fault category is output; a ZOA optimization algorithm is used to optimize hyper-parameters of a deep learning model, an optimization model is obtained, the performance of the optimization model is evaluated, an optimal model is obtained, and the prediction probability of each fault category is output, so that false report and missing report of faults can be effectively reduced in a fault detection task, potential safety hazards caused by the fact that the faults are not found in time are reduced, and the fault detection efficiency is improved. Therefore, the stability and reliability of the power system are improved.

The invention discloses a chemical process fault diagnosis and detection method and system. Belongs to the field of mechanical equipment fault diagnosis, and comprises the following detection steps: de-noising collected data by using a de-noising diffusion probability model (DDPM), and reducing noise interference in signals, thereby providing a clearer data basis for subsequent fault diagnosis; then, a fault diagnosis model (AResNet-Linformer) is built, the AResNet model is used for carrying out feature extraction on the denoised signals, and then feature information is sent to a Linformer neural network model for fault diagnosis; in order to improve the accuracy of the model, an improved star-sparrow optimization algorithm is used to carry out parameter optimization on the established fault diagnosis model. According to the invention, faults in the chemical process can be effectively identified, fault types can be rapidly determined, and a powerful data basis is provided for prevention and maintenance work.

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 provides a transformer substation secondary equipment fault positioning method based on a space-time diagram convolutional network model, and the method comprises the steps: extracting feature information based on the historical fault data of transformer substation secondary equipment, and arranging the operation state of the secondary equipment, the SV / GOOSE signal receiving state and a sampling value according to a time sequence to generate a fault feature set; based on the change trend of the fault feature set in the time sequence, performing multi-modal feature fusion on the fault feature data; constructing an STGCN model based on the fault features; historical fault samples are used for training an STGCN model, features highly related to faults are extracted through data preprocessing and feature screening, an optimized feature set is constructed, the fault positioning precision of the model is continuously improved, and the association mapping relation between fault feature information and fault categories is comprehensively established; and in combination with a fault inference rule, the feature set is input into the STGCN model, the secondary equipment fault is accurately positioned by using the space-time diagram convolution capability of the model, and the function of improving the accuracy and efficiency of transformer substation secondary equipment fault diagnosis is realized.

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 provides a small sample incremental fault diagnosis method based on a prototype adaptive network, and the method comprises the steps: S1, obtaining and processing data at a basic stage, preprocessing the data, and dividing the data into a pseudo basic data set and a pseudo incremental data set; s2, training a pseudo feature extraction network, training the PMCNN through a pseudo basic data set in a pseudo basic stage, and providing feature embedding for the PAN; s3, PAN training: training PAN in a pseudo-increment stage through a pseudo-increment data set to update the preliminary prototype representation; s4, training a feature extraction network, and training the MCNN by using the complete basic data set; s5, feature extraction in an increment stage: extracting features of a new fault category through MCNN and solving prototype representation of the new fault category; and S6, updating and classifying the prototype representation, updating the prototype representation by using PAN, and performing fault classification based on cosine similarity. According to the method, the fault diagnosis accuracy can be remarkably improved under the small sample condition, meanwhile, the memory ability of the deep learning model for old categories is kept, and the method is suitable for complex industrial fault diagnosis scenes.

The invention discloses a pellet fan fault diagnosis method, and aims to solve the problems of low efficiency and insufficient accuracy caused by the fact that traditional fan fault diagnosis depends on manual inspection and experience judgment. The method comprises the following steps: firstly collecting fan operation parameters to form a data set, after data cleaning and normalization processing, carrying out data enhancement by adopting random sampling and Gaussian noise addition, carrying out feature extraction by utilizing a ConvNeXt network, dividing the data set, calculating a fault category feature prototype vector, and predicting a result through distance measurement and classification decision. An L2 regular term is added in a prototype network model loss function, model parameters are adjusted through back propagation, the model is deployed after performance evaluation of a test set is stable, preprocessing and enhancing steps are repeated on real-time data, and if a prediction result is not consistent or new data exist, the model is retrained. According to the method, the diagnosis accuracy and efficiency are improved through multi-technology cooperation, the over-fitting of the model is effectively prevented, the fault feature capturing capability is improved, and the advantages of function perfection and technical perspectiveness are shown.

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 power distribution network coefficient complex fault evolution traceability analysis method and system, and the method comprises the steps: obtaining a first signal of a target power distribution line, and carrying out the first preprocessing of the first signal, and obtaining a second signal; establishing a first characteristic matrix, and obtaining a third signal according to the second signal; and inputting the third signal into a pre-trained first judgment network, obtaining the fault category of the fault point, and performing fault position evolution traceability. Through time reversal operation, the focusing performance of a power grid fault signal at a fault point is remarkably enhanced, in combination with BLT hypermatrix modeling, amplitude attenuation, phase change and energy reflection characteristics of signal propagation in a complex power grid are accurately described, and through deep feature learning of a Siamese network, in combination with energy focusing verification and least square optimization, the power grid fault signal is obtained. And the fault classification and position precision in a complex fault scene is improved.

The invention provides a power equipment fault detection method and system based on a deep learning network, and the method comprises the steps: obtaining state parameter data of power equipment in an observation time period, forming a first multi-dimensional time sequence according to the state parameter data, inputting the first multi-dimensional time sequence to a deep learning model, obtaining a fault probability prediction value corresponding to each time node; obtaining a fault coefficient according to the fault probability predicted values of all the time nodes, and judging whether the power equipment has a fault according to the fault coefficient; and if the power equipment has a fault, inputting the fault probability prediction value corresponding to each time node into the fault classification model to obtain a fault category set corresponding to the observation time period, and obtaining the fault category of the current time node according to the fault category set. According to the method and the device, the condition of inaccurate diagnosis result caused by data exception due to fault judgment through single-dimension data can be avoided, and the fault diagnosis accuracy 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 discloses a hierarchical management method and system for faults of a multi-motor servo actuator, and belongs to the technical field of servo actuator control. The method comprises the following steps of: sequentially dividing the faults of the multi-motor servo actuator into three fault layers from a bottom layer to a top layer according to fault objects, and determining fault types contained in each fault layer; external sensor signals and hardware signal trigger information are collected, and after filtering, conditioning and data fusion, the feature state of the multi-motor servo actuator is extracted; and in combination with a preset fault category judgment rule and a fault response action strategy, analyzing the feature state based on a machine learning algorithm to determine a fault category and a fault mark, and obtaining a corresponding fault response action strategy. According to the multi-motor servo actuator fault hierarchical management method and the multi-motor servo actuator fault hierarchical management system, the fineness and the efficiency of fault management and the stability and the reliability of the system are improved.

The invention discloses a meta-learning fault diagnosis and identification method based on Brown covariance. The method comprises the following steps: firstly, dividing input source domain data into a support set and a query set according to meta-tasks, and taking target domain data as the query set; then, deep fault features with identification capability are extracted through a Brown covariance feature extraction method; then, the extracted features are input into a prototype network, and fault prototype representations of all categories are generated; and finally, calculating the similarity between the query sample and the category prototype through a high-dimensional measurer, and outputting a fault category. According to the method, the difference of equipment data under different working conditions is fully considered, and efficient classification and accurate prediction in a complex environment are ensured in combination with a Brown covariance feature extraction method, a prototype network and a high-dimensional measurer.

The invention discloses an escalator fault diagnosis method based on multi-sensor data input. The escalator fault diagnosis method comprises the four steps of escalator original data collection, data preprocessing, fault data set setting, escalator fault recognition and cloud model updating. According to the data preprocessing, escalator signal data are filtered through an improved wavelet threshold noise reduction method; the quality of escalator signal data is improved; the fault data set is set by fusing fault data of all parts and all types of the escalator, full utilization of the fault data of the escalator is achieved, and the richness of fault category signals is improved. According to fault identification, multi-sensor data is input into a CNN-GRU network, and the CNN-GRU network extracts features in the input data and obtains a final identification result. According to cloud model updating, the CNN-GRU model is optimized by using CFOA, a network model with the optimal CNN-GRU is constructed, the recognition precision is high, the generalization performance is good, and the method can be widely applied to the field of escalator fault diagnosis.

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 discloses an orbit fault diagnosis method and system, and the method comprises the steps: calculating a sample entropy, an energy entropy, a power spectrum and a permutation entropy of an optimal solution # imgabs0 #, selecting an optimal entropy from the sample entropy, the energy entropy, the power spectrum and the permutation entropy according to a preset optimal entropy weight selection strategy, and extracting typical features according to the optimal entropy; an improved CNN-BiLSTM-SA neural network is constructed, and sequence space features are extracted according to the improved CNN-BiLSTM-SA neural network; hyper-parameters of the CNN-BiLSTM-SA neural network are optimized and improved according to a Cauchy-Euclidean clustering particle swarm optimization algorithm, and an orbit fault diagnosis model is obtained; the optimal solution # imgabs1 # of the steel rail vibration signals, the typical features and the sequence space features are fused to obtain fused features, the fused features are input into a rail fault diagnosis model, and the rail fault diagnosis model outputs and obtains a rail fault diagnosis result. The accuracy and efficiency of track fault category diagnosis can be improved.

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.