ABSTRACT
Progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data. However, machines often operate with various working conditions or the target task has different distributions with the collected data used for training (the domain shift problem). Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning based (UDTL-based) IFD problem. In this chapter, a taxonomy is constructed and a comprehensive review of UDTL-based IFD according to different tasks is performed. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD, influence of backbones, negative transfer, physical priors, etc.
