Motor fault diagnosis method based on depth auto-encoder and feature optimization

Abstract

The invention provides a migratable motor fault diagnosis method based on a depth auto-encoder and domain invariant feature extraction, and the method comprises the steps: carrying out the signal processing and mixed domain feature extraction of a motor vibration signal through dual-tree complex wavelet packet transformation; secondly, providing a domain invariant feature selection method based on importance scores and inter-domain difference measurement, and selecting features with high fault resolution capability and domain invariant characteristics; then, pre-training a depth automatic encoder (a source model) by adopting the selected domain invariant features so as to enhance the fault characterization capability of the depth features; and migrating the parameters of the source model to the target model with the same structure, and finely adjusting the target model by using the normal state feature data of the target domain. And finally, classifying target domain faults by adopting the fine-tuned target model. According to the method provided by the invention, the cross-domain fault diagnosis accuracy can be obviously improved, and the method has stronger availability, stability and advantages in an actual industrial scene.

Description

technical field [0001] The invention relates to the field of fault diagnosis, in particular to a motor fault diagnosis method based on deep self-encoder and feature optimization. Background technique [0002] With the rapid development of modern industry, rotating machinery such as motors is developing towards integration and complexity. At the same time, the working conditions are more complicated and harsh, so the failure rate of equipment is high. The failure of one component usually leads to the failure of other components. It may even cause huge human and economic losses. Therefore, it is of great practical significance to study the intelligent fault diagnosis method of the motor for the actual industrial scene. [0003] In recent years, research in the field of intelligent fault diagnosis generally includes frameworks based on Traditional Machine Learning (TML), Deep Learning (DL) and Transfer Learning (TL). The TML-based framework consists of three steps: signal pro...

AI Technical Summary

Problems solved by technology

However, most DL-based frameworks have limitations: the premise of the same distribution of the training set and the test set makes the generalization ability of the model insufficient; a large amount of labeled data is required, and when the labeled data is insufficient, overfitting will occur during diagnosis, resulting in a lower diagnostic accuracy. decline

At present, the deep migration model has achieved many successful applications in the cross-domain fault diagnosis of rotating machinery, but how to improve the fault representation ability of deep features and the stability of the diagnosis model under different working conditions is still a challenging task.

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