Cross-domain meta learning fault diagnosis based on multi-scale dilated convolution and adaptive relation module

For the established fault classification system, new faults cannot be identified due to lack of training data in the process of equipment operation. Aiming at the problems of multi-classification, small samples, and cross-domain brought by the new faults, one meta learning intelligent fault diagnosis method is proposed based on multi-scale dilated convolution and relation module. Firstly, multi -scale convolution is utilized to improve the feature extraction effectiveness in the extraction module. Subsequently, the fusion module is designed by dilated convolution and stochastic pooling. Finally, the relation module is employed to evaluate the distance between samples for fault diagnosis. Crucially, the meta learning strategy is executed to transform the training set into multiple tasks to train the proposed method. The superiority and effectiveness of the proposed method is validated by bearing and gearbox experiments with a few labeled fault samples. For the bearing fault diagnosis test, the verification results show that the accuracy rate of this method is 95.11% in 8way 1-shot, which is increased by 6.15% on average.(c) 2022 Published by Elsevier B.V.

Automated summary

A meta learning intelligent fault diagnosis method is proposed to address the problem of new faults not being identified due to lack of training data in the process of equipment operation. The method utilizes multi-scale dilated convolution and relation module for feature extraction and fault diagnosis. The training set is transformed into multiple tasks using meta learning strategy to train the proposed method, which is validated through bearing and gearbox experiments.