26136results about "Machine part testing" patented technology

The application provides a fault prediction method and apparatus, an electronic device and a storage medium. The method comprises: obtaining a vibration signal collected by a sensor installed on a target component of a target device, and cleaning and segmenting the vibration signal to obtain a target vibration signal; inputting the target vibration signal into a target anomaly judgment model to obtain an output result, and according to the output result, determining whether the target vibration signal is abnormal to obtain an anomaly judgment result; when the abnormal judgment result shows that the target vibration signal is abnormal, inputting the target vibration signal into a target fault prediction model to obtain a fault prediction result, wherein the target fault prediction model is obtained by combining an encoder of the target anomaly judgment model and a newly added full connection layer. The application solves the problem in the prior art that a faulty device cannot be timely maintained because the fault can only be monitored when the device fault is quite obvious.

Provided is a rolling bearing fault diagnosis method based on FFT coding and L-CNN, including: obtaining original bearing fault vibration data, extracting intrinsic mode components of different frequency bands in the original bearing fault vibration data, calculating a permutation entropy value corresponding to each of the intrinsic mode components, and performing wavelet threshold denoising according to the permutation entropy value to obtain a denoised reconstructed time domain signal; performing fast Fourier transform to the denoised reconstructed time domain signal to obtain a frequency domain signal and a phase angle corresponding to the time domain signal, reconstructing the frequency domain signal according to a preset rule, retaining frequency domain data of features of the phase angle, and drawing FFT-x heat maps of different fault type data according to an amplitude range; and constructing an model L-CNN, and inputting coded data into the L-CNN model, and obtaining fault diagnosis results.

Disclosed in the present invention are an adaptive deep transfer fault diagnosis method and system, an apparatus and a medium. The method comprises the following steps: S1: collecting vibration acceleration signals of industrial equipment under different working conditions, and dividing same into a source domain data set and a target domain data set; S2: building a self-tuning universal domain adaptive fault diagnosis model, which comprises a shared feature extractor, a known classifier and a plurality of unknown classifiers; S3: separately calculating a classification loss of known faults of the source domain, a discriminative loss of the plurality of unknown classifiers, a target domain soft consistency regularization loss and an information entropy maximization loss; S4: introducing a dynamic weighting strategy based on model uncertainty assessment to optimize the model parameters; and S5: using the model for diagnosis. The present invention can fully mine valid information in data, can establish reliable class decision boundaries, and in addition, uses the self-tuning dynamic update strategy to adjust weightings corresponding to different loss functions, thus allowing for quick generalization of the model to different industrial diagnosis scenarios.

The invention provides a coal mill fault diagnosis and prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: collecting a vibration signal of a coal mill bearing through a vibration sensor, carrying out the multi-scale analysis through wavelet decomposition, extracting features, constructing a deep convolutional neural network model, and carrying out the fault diagnosis and prediction of a coal mill bearing. A maximum mean value difference adaptive layer is introduced to carry out feature alignment on data of a source domain and a target domain, identification of fault types and degrees is realized, a health state evaluation index including a fault severity coefficient and a development trend coefficient is established, and the remaining service life is calculated and early warning is carried out. According to the invention, the accuracy and generalization ability of fault diagnosis are improved, accurate prediction and timely early warning of the bearing fault of the coal mill are realized, and the service life of equipment is prolonged.

The invention discloses a wind power bolt state detection method and system, and belongs to the technical field of mechanical structure testing and health monitoring, and the method comprises the steps: obtaining the real-time strain data of each monitoring point; acquiring operation condition data of the unit in real time; dynamically generating a finite element model of the tower drum structure according to the operation condition data; the finite element model and the real-time strain data are combined to generate a stress distribution map of the whole tower cylinder bolts; inputting the stress distribution atlas into a preset fatigue damage model, calculating a bolt life attenuation rate and positioning an external load abnormal source; and when it is detected that the life attenuation rate exceeds a preset threshold value, generating a grading early warning signal and outputting a maintenance strategy according to a positioning result. According to the method, a wireless strain sensor network, dynamic finite element model correction and multi-dimensional fatigue damage analysis technology are adopted, a whole tower barrel stress distribution map is generated in real time, external working condition data are associated, the bolt service life attenuation rate can be accurately evaluated, a load abnormal source can be positioned, and a graded maintenance strategy can be output.

The invention discloses a vacuum dry pump bearing fault diagnosis method based on domain confrontation and attention transfer learning, and aims to solve the problem of performance reduction caused by data distribution difference of dry vacuum pump bearing faults in cross-domain fault diagnosis of a deep learning method. Domain adversarial training is carried out by introducing a gradient inversion layer, and feature weights are dynamically adjusted in combination with an attention mechanism. According to the method, the distribution difference between the bearing fault data source domain and the target domain can be effectively reduced, and the fault diagnosis precision of the model on the target domain is improved. The method specifically comprises the steps of constructing a deep transfer learning model combining a convolutional neural network, an attention mechanism and domain adversarial training, performing feature extraction and classification training on data of a source domain and a target domain, and optimizing the model by adopting cross entropy loss, domain adversarial loss and maximum mean difference loss. The method shows high diagnosis accuracy and good generalization ability under different working conditions, and is suitable for cross-domain fault detection and diagnosis.

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention discloses a bolt looseness online monitoring method and system, and the method comprises the steps: collecting an original data set containing a vibration signal of bolt connection, temperature gradient data and structural stress distribution through a multi-mode sensor array, and carrying out the time-space alignment and frequency domain decomposition processing of the original data set, obtaining a multi-dimensional feature matrix of the bolt nodes; based on the multi-dimensional feature matrix, a graph neural network model is adopted to carry out bolt looseness probability calculation, and a real-time looseness probability value of each bolt is output; generating a risk level map based on time evolution according to the real-time loosening probability value; and the risk level map is mapped in real time through a three-dimensional visual interface, and when it is detected that the bolt loosening risk level exceeds a preset threshold value, an early warning message is generated and uploaded to an operation and maintenance platform, and closed-loop monitoring response is completed. According to the embodiment of the invention, accurate assessment, dynamic prediction and visual early warning of the loosening risk can be realized, and the intelligent level of structure safety monitoring is improved.

The invention discloses a hydropower station gate detection method and system, and relates to the technical field of hydropower station gate detection, and the method comprises the steps: collecting environment parameters, building a dynamic environment model, and determining a real-time impact factor on a gate state; synchronously collecting gate vibration and sound wave data to obtain a composite signal; constructing a short-term and long-term trend model, and identifying a potential abnormal trend; automatically adjusting the sampling frequency of the sensor according to an abnormal detection result; performing fault classification on the multi-parameter signals by using an event pattern recognition algorithm; and generating an automatic response strategy according to a fault classification result. According to the hydropower station gate detection method provided by the invention, the accuracy and efficiency of hydropower station gate detection are improved through multi-dimensional parameter fusion and adaptive sampling adjustment. Through monitoring of various parameters, the system can comprehensively analyze the state of the gate, the misjudgment risk caused by a single signal is reduced, and comprehensive recognition of faults is enhanced.

A method to detect anomaly in mechanical components based on sound, vibration, and temperature analysis is disclosed. The method includes receiving sound, vibration, and temperature data from the mechanical components in real-time. Further, the method includes generating synthesized sound data from the received sound data based on varying noise environments by performing pitch changing, temporal stretching, and noise injection. Furthermore, the method includes preprocessing the received data and the synthesized sound data to remove background noise and thermal shifting. Moreover, the method includes extracting one or more features from the preprocessed data. Additionally, the method includes identifying anomaly in the mechanical components based on the extracted one or more features by employing one or more Machine Learning (ML) models. The method also includes rendering at least the identified anomaly in the mechanical components to a user.

The present invention relates to an inter-shaft bearing fault diagnosis method and apparatus under a complex working condition. The apparatus comprises: a data collection module, which collects an inter-shaft bearing vibration signal by means of mounting an acceleration sensor; a data preprocessing module, which converts the collected vibration signal into a time-frequency graph, so as to express features of the signal in terms of a time domain and a frequency domain; a feature extraction module, which effectively captures a complex mode and correlation from a converted signal by means of combining an attention mechanism and a deep residual network, so as to improve the accuracy of fault diagnosis; and a model verification module, which trains the feature extraction module on the basis of a constructed data set, and evaluates a model effect on the basis of a verification set. The inter-shaft bearing fault diagnosis method and apparatus under a complex working condition in the present invention can improve the accuracy of fault diagnosis of a bearing under a complex working condition.

The invention belongs to the technical field of bearing residual service life detection, and discloses a rolling bearing residual service life prediction method and system based on a convolution white box. According to the method, time domain and frequency domain feature extraction, noise reduction processing and health state and degradation state division are carried out on an original vibration signal, and deep neural network training is carried out in combination with a Weibull-MSE loss function, so that prediction of RUL conforms to the actual degradation process of a bearing; and synchronous extraction of long-time dependence information and local degradation characteristics of the bearing signal is completed through a convolution CRATE network architecture fusing an expansion causal convolution attention mechanism DCA and a multi-scale convolution MSC. According to the method, the accuracy of RUL prediction is improved in combination with health state evaluation, the local feature extraction capability is enhanced, the local modeling capability of the model is improved through multi-scale information fusion, and the interpretability and the prediction credibility are improved through a CRATE structure.

A rolling bearing combined fault diagnosis method based on a multi-domain feature construction is provided, including: collecting vibration signals of rolling bearings, extracting time-domain and frequency-domain features of fault-sensitive vibration signals of the rolling bearings, and obtaining an in-domain feature set; performing an improved empirical mode decomposition on the vibration signals of the rolling bearings to obtain time-frequency features; constructing a multi-domain fault sample set based on the in-domain feature set and the time-frequency features; performing a fault mechanism analysis on the multi-domain fault sample set to obtain modulation features under each single fault; carrying out a feature engineering on the time-domain and frequency-domain features, and screening out relevant features, redundant features and irrelevant features; performing an unsupervised hierarchical clustering on test set fault samples, and then inputting the test set fault samples into a KNN model for a classification, and obtaining rolling bearing combined fault diagnosis results.

The invention discloses an attention mechanism and LSTM fused industrial bearing vibration time sequence signal fault prediction method and system. The method comprises the following steps: collecting a bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing; constructing a deep learning model combining the bidirectional BiLSTM and a coordinate attention mechanism to extract bidirectional time sequence features and enhance key fault features; carrying out model training by adopting a multi-target composite loss function and an Adam optimizer, and introducing an early stop mechanism to prevent overfitting; performing fault type identification and degree evaluation on the real-time vibration signal by using the trained model, and performing quantitative analysis by fusing multi-scale spectrum kurtosis features and nonlinear kinetic parameters; and finally, outputting a fault diagnosis report, and triggering multi-stage early warning based on an adaptive threshold. The method can realize high-precision and high-reliability bearing fault prediction and health state evaluation, and is suitable for intelligent operation and maintenance of industrial equipment.

The invention relates to the technical field of bearing service life prediction, in particular to a bearing residual service life prediction method based on an improved Attention-GRU model, and the method comprises the steps: monitoring full-life vibration data of a bearing when the bearing runs until the bearing is damaged, carrying out the preprocessing of the data, and dividing the data into a training set and a test set; time-frequency domain features are extracted from the vibration data and feature fusion is carried out to obtain a fused feature set; and constructing a bearing residual life prediction model. And inputting the fused feature set into a life prediction model for training and prediction to obtain the remaining service life of the bearing. According to the residual life prediction model in the scheme, time-frequency domain information extraction is carried out on bearing data through discrete wavelet down-sampling and Fourier frequency domain feature extraction, and then the receptive field of the bearing data in the channel and space is improved through channel and space attention feature enhancement; and finally, predicting and outputting the health index of the predicted time step through Attention-GRU regression, and combining the health index with the full-life data to obtain the predicted residual life.

The invention discloses a bridge health prediction device based on a Beidou system and a monitoring analysis method, and relates to the field of bridge health monitoring. The method comprises the following steps: S1, deploying monitoring points at key structure nodes of a bridge, and collecting three-dimensional position information and multi-source data by using a Beidou system and a multi-type sensor; s2, constructing a time-varying displacement field and a continuous deformation tensor, fusing stress and strain to construct a structural fatigue factor, and introducing environmental disturbance to realize dynamic correction of the fatigue factor; s3, a health state function is constructed based on the stress response, the displacement gradient and the deformation tensor, a damage mapping function is constructed in combination with accumulated deformation and a historical threshold value, and a damage thermodynamic diagram is generated; and S4, finally fusing fatigue, health and damage indexes to evaluate bridge risks, and performing dynamic early warning. Through fusion of mechanical tensor features and environmental disturbance modeling, a multi-index linkage fatigue and damage assessment mechanism is constructed, and the precision, robustness and global perception ability of bridge health monitoring are improved.

The invention discloses a bolt on-line monitoring method and system, and the method comprises the steps: generating a micro-motion feature matrix of a bolt according to a multi-dimensional vibration signal and a stress wave propagation characteristic of a bolt connection part; based on the micro-motion characteristic matrix, fusing local stress distribution data acquired by a bolt surface strain gauge, and outputting a health state quantitative index of the bolt; outputting a loosening risk level according to the health state quantitative index and historical loosening evolution data; and based on the looseness risk level and in combination with equipment operation state parameters, a self-adaptive decision tree model is adopted to dynamically adjust monitoring frequency and an alarm threshold value, a real-time monitoring strategy and a visual early warning report are generated, and a wireless transmission module is synchronously triggered to upload the real-time monitoring strategy and the visual early warning report to a cloud operation and maintenance platform. According to the embodiment of the invention, high-precision, self-adaptive and traceable bolt health state evaluation and early warning can be realized.

The invention discloses an electromechanical equipment health assessment and early warning method based on multi-mode dynamic perception, and belongs to the field of intelligent operation and maintenance of electromechanical equipment. The problems that in the prior art, a single physical quantity cannot comprehensively reflect the equipment state and a traditional signal processing algorithm cannot adapt to the equipment degradation mode change are solved, a panoramic sensing system covering multiple physical fields such as vibration, temperature and noise is constructed through a multi-mode sensor network and a dynamic weight fusion algorithm, and the multi-physical-field multi-physical-field panoramic sensing method is applied to the multi-physical-field multi-physical-field panoramic sensing system. The problem of isolated island of traditional single-dimensional monitoring information is solved; through a physical-depth mixed feature extraction architecture, combining interpretable engineering features with abstract features extracted by a deep neural network to form a health assessment model with mechanism transparency and mode generalization ability; through deep integration of the digital twin platform and the RPA technology, the manual inspection frequency and workload are reduced, the fault recognition accuracy is promoted to increase year by year, and continuously optimized intelligent operation and maintenance ecology is formed.

The invention discloses a bearing residual life prediction method based on a multi-teacher element weight knowledge distillation network, and the method comprises the following steps: (1) carrying out the normalization, noise reduction and segmentation of a multi-dimensional vibration time sequence signal collected by an original sensor, and dividing the signal into a training set and a verification set; (2) inputting the processed training set data into a multi-teacher element weight knowledge distillation network, training teacher models, and fixing parameters of the three teacher models after training is completed; (3) performing knowledge distillation training on the student model by using an adaptive meta-weight strategy gradient learning algorithm; and (4) the student model after distillation training is used for bearing residual life prediction. According to the method, the number of network model parameters is small, the calculation complexity is low, the prediction precision is high, and the method can be actually deployed on edge equipment.

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

The invention relates to the technical field of structure monitoring, in particular to a safety monitoring method and device for a steel structure. The method comprises the following steps: collecting historical key environment data from a local meteorological station, distributing weights according to the influence degree of the key environment data on a steel structure, and generating an environment index; evaluating vibration response characteristics of the steel structure under the dynamic load in combination with the environment index, and generating a vibration response index; performing static and dynamic analysis on the steel structure in combination with the vibration response index to obtain a structural stability index; and generating a comprehensive safety coefficient by combining an environment index, a vibration response index and a structural stability index, dividing risk levels, and providing specific maintenance and transformation suggestions for different levels. According to the safety monitoring method and device for the steel structure, the safety coefficient can provide a more comprehensive steel structure health condition index, and potential risks in multiple aspects can be identified, so that potential safety hazards can be found in time.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention discloses a variable working condition migration bearing fault diagnosis method based on a ViTCapsNet network in a noise environment, and the method comprises the steps: firstly obtaining vibration signal data of a rolling bearing in different fault states under different loads, and adding Gaussian white noises of different degrees; performing continuous wavelet transform on the data added with the noise to obtain a two-dimensional wavelet image, and dividing the two-dimensional wavelet image into a training set and a test set; a ViT Encoder model is established, position information capable of being trained is added by using an Embedded module, position features are embedded into the model, and global information in the features is extracted through a multi-head self-attention mechanism in the Encoder module; constructing a capsule network which comprises a primary capsule layer and a digital capsule layer, further extracting spatial information in the features, and performing accurate fault diagnosis; and finally, performing visual interpretation on the features processed by the Encoder module by using Grad-CAM, and completing efficient migration fault diagnosis on a test data set.

The invention discloses a rotating machine fault diagnosis method, which comprises the following steps of: acquiring vibration, temperature, acoustic emission and current signals at key parts of a rotating machine, and extracting characteristic parameters such as time domain and frequency domain after preprocessing such as filtering and noise reduction; and inputting the characteristic parameters into machine learning models such as a support vector machine, combining deep learning models such as a convolutional neural network and a long-short-term memory network, performing comparative analysis by using a digital twin model, and fusing diagnosis results to output fault types, positions, severity and maintenance suggestions. The method overcomes single diagnosis limitation, multi-source signal complementation, multi-model collaboration, accurate fault diagnosis and diagnosis reliability improvement, provides a scientific basis for equipment maintenance, and is of great significance for guaranteeing safe operation of rotating machinery, reducing maintenance cost and promoting industrial intelligent development.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention discloses a pre-twisted damper fatigue life prediction method for an extra-high voltage ground wire, and particularly relates to the technical field of power transmission line vibration prevention. Collecting multi-source vibration and environment data, and constructing a vibration characteristic time sequence; amplitude, frequency and strain energy are extracted based on the multi-frequency vibration response, and a multi-frequency collaborative vibration model is established; obtaining a damper structure and material parameters, establishing a finite element model, coupling the finite element model with a vibration model, and simulating local stress response; a Rainflow counting method and a Miner damage criterion are adopted to construct a fatigue damage factor distribution matrix; predicting the residual fatigue life of the damper based on the damage evolution trend, and evaluating the replacement opportunity; according to the method, accurate fatigue life prediction and optimal replacement strategy recommendation of the pre-twisted damper under complex working conditions can be realized, and the method has high precision, high adaptability and engineering practicability.

The invention discloses a bearing fault diagnosis method based on a parallel multi-fusion deep learning network model. And obtaining a sample data set, dividing the sample data set into a training data set and a test data set, carrying out parallel multi-fusion deep learning network model training by using the training data set, and inputting the test data set into the trained parallel multi-fusion deep learning network model to carry out fault diagnosis. Wherein the parallel multi-fusion deep learning network model comprises three parallel sub-networks, namely a bidirectional gating circulation unit sub-network, a fast Fourier transform-convolutional neural network sub-network and a discrete wavelet transform-enhanced depth separable ion network, and a dynamic multi-head attention mechanism module. According to the method, the three parallel sub-networks and the dynamic multi-head attention mechanism module are combined to automatically extract and identify the fault features of the rolling bearing under multiple working conditions, and self-adaptive fault feature extraction under different domain signals and rolling bearing fault diagnosis under complex working conditions are realized.

The invention belongs to the technical field of bearing fault diagnosis, and particularly relates to a transmission casing typical small bearing fault weak vibration signal extraction method, which comprises the following steps: reconstructing a bearing vibration signal to obtain a modulation impact signal, and calculating a weighted kurtosis value based on the modulation impact signal to improve a Protugram algorithm; noise interference is added to a bearing fault signal, and extraction of a small bearing fault weak impact signal is realized by optimizing a filtering frequency band based on a weighted kurtosis value improved Protugram algorithm; bearing impact characteristics can be enhanced based on a simulation sensor resonance enhancement algorithm (SPM); the impact quantification of the bearing can eliminate the influence of different rotating speeds and load working conditions, and realizes the quantitative diagnosis of bearing faults. And finally, the fault data of the rolling body, the inner ring and the outer ring of the flight attachment case small bearing part test bed are verified, and effective extraction and enhancement of bearing fault weak signals can be realized.

The invention discloses a gearbox internal fault monitoring method, and mainly relates to the field of fault monitoring. Comprising a vibration sensor, a temperature sensor and an acoustic sensor and is used for collecting vibration signals, temperature signals and acoustic signals in the operation process of the gearbox. The system has the beneficial effects that various types of sensors are mounted at multiple key parts of the gear box, various operation data such as vibration, temperature and acoustics are comprehensively acquired in real time, and subtle changes generated by initial faults in the gear box can be sensitively captured by applying advanced signal processing and feature extraction technologies. According to the monitoring method, the characteristics can be accurately extracted and identified, so that early warning can be timely given out when the fault is in the germination stage, further deterioration of the fault and sudden shutdown of equipment are effectively avoided, and the maintenance cost and the production loss caused by shutdown are greatly reduced.

The invention relates to the technical field of computer deep learning, and relates to an equipment fault diagnosis network model training method based on multi-source domain knowledge joint migration, and the method comprises the steps: constructing a training sample set, a verification set and a test set, and training a pre-constructed equipment fault diagnosis network model through the training sample set, optimizing parameters of the equipment fault diagnosis network model to obtain a trained equipment fault diagnosis network model; verifying the trained equipment fault diagnosis network model by adopting a verification set, evaluating the diagnosis performance of the trained equipment fault diagnosis network model, and selecting an optimal equipment fault diagnosis network model; and testing by adopting the test set optimal equipment fault diagnosis network model, and evaluating the performance of the optimal equipment fault diagnosis network model. According to the method, multi-source-domain cooperative training is carried out on the model by utilizing a collected multi-source-domain data set which is composed of a plurality of labeled source-domain data sets and unlabeled source-domain data sets and contains a small number of labels, domain invariant features in fault samples are learned, and shared diagnosis knowledge is migrated to a target domain from labeled data and unlabeled data together; therefore, the trained equipment fault diagnosis network model can complete a cross-domain fault diagnosis task without obtaining a target sample in advance.