Bearing Fault Diagnosis Method Based on Embedded Discriminative Conditional Adversarial Domain Adaptation

Abstract

The present disclosure discloses a bearing fault diagnosis method based on embedding discriminative conditional confrontation domain adaptive, which includes: respectively collecting first and second vibration signals of high-speed rail traction motor bearings operating under different working conditions, and converting the first vibration The signal is used as the source domain data, and the second vibration signal is used as the target domain data; a conditional confrontational domain adaptive network including a feature extractor F, a label predictor G and a domain classifier D is established; at the same time, the source domain data with labels and no The labeled target domain data is used to train the network; after the training is completed, the unlabeled target domain data is input into the trained conditional adversarial domain adaptive network for forward propagation, and the trained conditional adversarial domain adaptive network outputs a failure Prediction results to realize high-speed rail traction motor bearing fault diagnosis. The disclosure can effectively improve the diagnostic accuracy of the model on the target domain data, reduce inter-domain differences and enhance the distinction of feature distribution, thereby improving the robustness of the model.

Description

technical field [0001] The disclosure belongs to the field of mechanical fault diagnosis, and in particular relates to a bearing fault diagnosis method based on embedded discriminative condition confrontation domain self-adaptation. Background technique [0002] In recent years, deep learning has been widely used in intelligent fault diagnosis due to its powerful feature extraction ability and ability to process large data. However, due to frequent changes in the working conditions of high-speed rail traction motors, the assumption that the training samples and test samples of deep learning are subject to independent and identical distribution is broken, and the diagnostic accuracy of deep learning is greatly reduced when it is applied to the fault diagnosis of high-speed rail traction motors. The existing depth-domain adaptive methods cannot fully reduce the inter-domain differences because they do not use label information. Moreover, the traction motors of high-speed railw...

AI Technical Summary

Problems solved by technology

However, due to frequent changes in the working conditions of high-speed rail traction motors, the assumption that the training samples and test samples of deep learning obey the independent and identical distribution is broken, and the diagnostic accuracy of deep learning is greatly reduced when it is applied to the fault diagnosis of high-speed rail traction motors.

The existing depth-domain adaptive methods cannot fully reduce the inter-domain differences because they do not use label information. Moreover, the traction motors of high-speed railways need to withstand the shock and vibration generated by the dynamic action of locomotive wheels and rails, resulting in noise and noise in the collected vibration signals. There are many interference components, so a model with strong robustness is needed

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