The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data

Han Cheng, Xianguang Kong, Qibin Wang, Hongbo Ma and Shengkang Yang

Reliability Engineering and System Safety, 2022, vol. 225, issue C

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.

Keywords: Remaining useful life prediction; The first predicting time identification; Deep transfer learning; Multiple working conditions (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002277

DOI: 10.1016/j.ress.2022.108581

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