A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition

Jiusi Zhang, Xiang Li, Jilun Tian, Yuchen Jiang, Hao Luo and Shen Yin

Reliability Engineering and System Safety, 2023, vol. 231, issue C

Abstract: Most supervised learning-based approaches follow the assumptions that offline data and online data must obey a similar distribution, which is difficult to satisfy in realistic remaining useful life (RUL) prediction. To solve the problem, domain adaptation (DA) learning-oriented transfer learning (TL) was proposed. Nevertheless, only adopting a conventional global DA approach may confuse the fine-grained features between subdomains represented by different degenerate stages. Consequently, a novel variational auto-encoder-long–short-term memory network-local weighted deep sub-domain adaptation network (VLSTM-LWSAN) is proposed for RUL prediction. Specifically, the input data are compressed into the interpretable latent space, from which the fine-grained features between subdomains are local alignment through local weighted deep sub-domain adaptation network. In this sense, the discrepancy between the unlabeled target domain and the source domain is decreased. The proposed VLSTM-LWSAN is verified by an aircraft turbofan engine dataset. The research results represent that the VLSTM-LWSAN outperforms some deep learning approaches without transfer learning and conventional transfer learning approaches.

Keywords: Remaining useful life; Transfer learning; Variational auto-encoder; Local weighted deep sub-domain adaptation; Prediction (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006019

DOI: 10.1016/j.ress.2022.108986

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