A systematic review of deep transfer learning for machinery fault diagnosis

Abstract

With the popularization of the intelligent manufacturing, much attention has been paid in such intelligent computing methods as deep learning ones for machinery fault diagnosis. Thanks to the development of deep learning models, the interference of the human experience can be greatly reduced, and the fault diagnosis accuracy can also be increased under certain conditions. To improve the generalization ability of the intelligent fault diagnostics, the deep transfer learning consisting of both transfer learning and deep learning components was accordingly developed. This paper reviews the research progress of the deep transfer learning for the machinery fault diagnosis in recently years. It is summarizing, classifying and explaining many publications on this topic with discussing various deep transfer architectures and related theories. On this basis, this review expounds main achievements, challenges and future research of the deep transfer learning. This provides clear directions for the selection, design or implementation of the deep transfer learning architecture in the field of the machinery fault diagnostics.