Siyu ZHANG 1, Lei SU1, Jiefei GU1, Ke LI1, Lang ZHOU2 and Michael PECHT3
1Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China
2HUST-Wuxi Research Institute, Wuxi 214071, China
3Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
For more information about this article and related research, please contact Prof. Michael Pecht
In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect enough large-scale supervised data to train deep networks. Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task, which performs well on small data and reduces the demand for high computation power. However, the detection performance is significantly reduced by the direct transfer due to the domain difference. Domain adaptation (DA) can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data. In this survey, we review various current DA strategies combined with deep learning (DL) and analyze the principles, advantages, and disadvantages of each method. We also summarize the application of DA combined with DL in the field of fault diagnosis. This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.
This article is available online here.