Deep Dynamic Adaptive Transfer Network for Rolling Bearing Fault Diagnosis With Considering Cross-Machine Instance

Abstract

The research of intelligent fault diagnosis method has made great progress. The prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant labeled data, which is difficult to satisfy in practice. Fortunately, we can obtain a large amount of rolling bearing failure data under laboratory conditions. Inspired by the idea of transfer learning, we propose a deep dynamic adaptive transfer network (DDATN) for intelligent fault diagnosis of rolling bearings. In addition to performing transfer diagnosis under different working conditions and failure degrees of the same type of bearing, it is also able to accomplish the task of cross-machine fault diagnosis from bearings under laboratory conditions to the bearings in practical applications. In the DDATN, the marginal probability distribution and conditional probability distribution of the data are aligned by dynamic domain adaptation using weight factor. First, the original vibration signal of the bearing is first processed to establish the source and target domains. Then, pseudo-label learning on target domain unlabeled data is performed and the transferable features between domains are extracted through the deep parameter-shared neural networks. Next, by performing dynamic adaptation on the extracted transferable features and optimizing the intelligent fault diagnosis model through backpropagation, the complete transfer diagnosis task in the target domain is accomplished. The effectiveness of the proposed DDATN method is demonstrated through variable working conditions and cross-machine transfer fault diagnosis tasks. Compared with other intelligent fault diagnosis methods, the proposed method shows clear advantages.