作者
Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen
发表日期
2021/9/29
来源
IEEE Transactions on Instrumentation and Measurement
卷号
70
页码范围
1-28
出版商
IEEE
简介
Recent 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 (UDTL)-based IFD problem. Although it has achieved huge development, a standard and open source code framework and a comparative study for UDTL-based IFD are not yet established. In this article, we construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD, which is rarely studied, including …
学术搜索中的文章
Z Zhao, Q Zhang, X Yu, C Sun, S Wang, R Yan, X Chen - IEEE Transactions on Instrumentation and …, 2021