Bearing fault diagnosis under variable working conditions based on deep residual shrinkage networks

Deep learning has been widely used in the field of rotating machinery fault diagnosis nowadays.To improve the diagnosis effect of the deep learning oriented to a large number of unlabeled data and variable working conditions,a network model combining the feature learning ability of deep learning and the generalization ability of transfer learning was constructed.The deep shrinkage residual network was constructed by adding soft thresholds to extract the characteristics of bearing vibration data under noise redundancy.Then,the Joint Maximum Mean Deviation (JMMD) criterion and Conditional Domain Adversarial (CDA) learning domain adapting network were used to align the source and target domains.At the same time,adding Transferable Semantic Augmentation (TSA) regular items improved alignment performance between classes.The proposed model was verified by three kinds of experiments:variable load,variable speed and variable noise,and the result proved that the proposed method could overcome the shortcomings of traditional deep learning and shallow transfer learning algorithms. © 2023 CIMS. All rights reserved.