An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions

Jichao Zhuang, Minping Jia and Xiaoli Zhao

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

Abstract: Many existing domain adaptation-based methods try to derive domain invariant features to address domain shifts and obtain satisfactory remaining useful life (RUL) of bearings under multiple working conditions. However, most methods may not consider local semantics about degradation features and mutual information from target-specific data when aligning distribution discrepancies, thus resulting in limitations. Additionally, the use of contrastive learning to maintain mutual information may introduce unstable negative samples. To overcome these issues, a metric adversarial domain adaptation approach (MADA) is proposed to evaluate the bearing RULs under multiple working conditions. More specifically, an adversarial domain adaptation architecture with a supervised positive contrastive module is developed to consider mutual information without a negative sample, further learning domain invariant features. Also, the dual self-attention module is designed to extract multi-scale contextual semantics between degradation features. Meanwhile, extensive experiments are conducted in twelve cross-domain scenarios for two bearing cases. The experimental results show that the proposed method is more competitive.

Keywords: Remaining useful life; Transfer learning; Domain adaptation; Multiple working conditions; Rolling bearing (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002447

DOI: 10.1016/j.ress.2022.108599

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