The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data. (September 2022)

• Record Type: Journal Article
• Authors: Cheng, Han; Kong, Xianguang; Wang, Qibin; Ma, Hongbo; Yang, Shengkang
• Is Part Of: Reliability engineering & system safety. Volume 225(2022)
• Publication Date: 2022-09
• Subjects: Remaining useful life prediction -- The first predicting time identification -- Deep transfer learning -- Multiple working conditions; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108581
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: The two-stage RUL prediction framework is investigated in this paper. The two-level alarm mechanism is proposed to detect FPT of each entity adaptively. DSCN-DTAM is built for cross-domain prognostic with incomplete target domain data. Double transferable attention mechanism is designed for the fined-grained transfer. Four transfer prognostic tasks verify the effectiveness of the proposed method. Abstract: The remaining useful life (RUL) prediction provides an essential basis for improving mechanical equipment reliability. In practical application, the variant of working conditions and incomplete degradation data seriously deteriorate the performance of the prognostic models. In order to conquer this problem, a two-stage RUL prediction method is proposed for the cross-domain prognostic task with insufficient degradation data. At first, the two-level alarm mechanism is employed to detect the first predicting time (FPT) of each mechanical entity adaptively. Then, the deep separable convolutional network with the double transferable attention mechanism (DSCN-DTAM) is proposed to construct the cross-domain prognostic model. In DSCN-DTAM, multiple regularization strategies can guide the model to extract domain-invariant features, and the double transferable attention mechanism is designed to select the degradation information with high transferability. Finally, the proposed method is verified by multiple transfer prognostic tasks designed by two bearing datasets. Compared with other methods, the proposed method shows superior performance.