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

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.

Automated summary

Most supervised learning-based approaches assume that offline data and online data should have a similar distribution, which is difficult to satisfy in realistic remaining useful life prediction. To overcome this issue, a new transfer learning method called domain adaptation learning-oriented transfer learning (TL) is proposed. The method, called VLSTM-LWSAN, uses a local weighted deep sub-domain adaptation network to align fine-grained features between different degenerate stages, reducing the discrepancy between the target and source domains. Experimental results on an aircraft turbofan engine dataset demonstrate that VLSTM-LWSAN outperforms deep learning approaches without transfer learning and conventional transfer learning methods.