Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

By Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, Chuang Sun, Shibin Wang, Ruqiang Yan, Xuefeng Chen. IEEE Transactions on Instrumentation and Measurement, 2019

505 citations recorded by the source corpus.

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 transferability of features, the influence of backbones, negative transfer, physical priors, and so on. To emphasize the importance and reproducibility of UDTL-based IFD, the whole test framework will be released to the research community to facilitate future research. In summary, the released framework and comparative study can serve as an extended interface and basic results to carry out new studies on UDTL-based IFD. The code framework is available at https://github.com/ZhaoZhibin/UDTL.

Subfields: Artificial Intelligence, Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/372ea3af1f195d77ac5ac700dbf4865040832e59

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lingfei Deng
2. Dongrui Wu
3. Wen Zhang
4. Zidong Wang
5. Lei Zhang
6. Andrea Gesmundo
7. Joseph Dean

In Control and Systems Engineering

1. Yihao Xue
2. Mengjie Huang
3. Rui Yang
4. Zidong Wang
5. Roberto Ferrero

Citations in the corpus

• A Survey on Negative Transfer (2020, background): “Using an attention mechanism [172], which re-weights the knowledge learned from previous tasks to help train a new task, has also been explored.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “[61] focused on unsupervised deep transfer learning on bearing fault diagnosis and provided baseline accuracy of widely used transfer learning methods”