Year: 2020
Type: article
Abstract: The development of sensor technology provides massive data for data-driven fault diagnosis. In recent years, more and more scholars are studying artificial intelligence technology to solve the bottleneck in fault diagnosis. Compared with other classification and prediction problems, fault diagnosis often faces the problem of data scarcity. To overcome the lack of fault data, the transfer learning based on different working condition is gradually introduced into fault diagnosis by scholars. This paper discusses the current mainstream AI-based fault diagnosis methods, and analyzes the advantage of transfer learning for fault diagnosis problem. Then, a transfer component analysis (TCA) based method is proposed to transfer data features between different working conditions. Through the TCA-based method, the fault diagnosis model under the working condition can be established with the help of historical working condition. It effectively alleviates the problem of data scarcity under the condition to be predicted. Different from other fault diagnosis studies, this paper considers the online maintenance process based on TCA. A fault diagnosis framework including online maintenance process is proposed. Finally, a case study of bearing diagnosis from Case Western Reserve University proves the feasibility and effectiveness of the proposed TCA-based method and our fault diagnosis framework.
Source: IEEE Access
Authors: Wei Xu, Yi Wan, Tian-Yu Zuo, Xin-Mei Sha
Institutions: Sanjiang University, Nanjing Xiaozhuang University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,750
Awards: G266545999, G2689223682, G979982651
OpenAlex ID: W3020128465