A fault diagnosis method of bearings based on deep transfer learning

Abstract

In recent years, many deep transfer learning methods have been widely used in bearing fault diagnosis under varying working conditions to solve the problem of data distribution shift. However, the deep transfer learning methods used in bearing fault diagnosis have low accuracy when source domain data differ much and feature distribution misaligns, so a deep multi-source transfer learning model is proposed in this paper. To solve the problem of the large discrepancy in source domain data, we use maximum mean discrepancy (MMD) to select suitable source domain data to form a new source domain to help model training, we utilize independent domain-specific feature extractors to extract domain features to avoid poor source domains affecting feature extraction of other domains. At the same time, classifier output is aligned using Wasserstein distance to reduce the probability of misclassification of boundary samples, and the weighted decision is used to impose more weight on the better source domains. In addition, aiming at the problem of data distribution misalignment, we propose an alignment method combining MMD, local maximum mean discrepancy (LMMD), and triplet loss. Experimental results show that the proposed model can achieve more than the accuracy of 95% in the four working conditions of the Paderborn bearing fault dataset, and is suitable for bearing fault diagnosis under varying working conditions.