Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines

authored by

Wentao Mao, Wen Zhang, Ke Feng, Michael Beer, Chunsheng Yang

Abstract

In recent years, deep transfer learning techniques have been successfully applied to solve RUL prediction across different working conditions. However, for RUL prediction across different machines in which the data distribution and fault evolution characteristics vary largely, the extraction and transition of prognostic knowledge become more challenging. Even if fault mode information can assist in the knowledge transfer, model bias will inevitably exist on the target machine with mixed or unknown faults. To address this issue from a transferability perspective, this paper proposes a novel selective transfer learning approach for RUL prediction across machines. First, the paper utilizes the tensor representation to construct the meta-degradation trend of each fault mode and evaluates the transferability of source domain data from fault mode and degradation characteristics through a new cross-machine transfer degree indicator (M-TDI). Second, a Long Short-Term Memory (LSTM)-based selective transfer strategy is proposed using the M-TDIs. The paper designs a training algorithm with an alternating optimization scheme to seek the optimal tensor decomposition and knowledge transfer effect. Theoretical analysis proves that the proposed approach significantly reduces the upper bound of prediction error. Furthermore, experimental results on three benchmark datasets prove the effectiveness of the proposed approach.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

Henan Normal University
Engineering Lab of Intelligence Business & Internet of Things of Henan Province
National University of Singapore
University of Liverpool
Tsinghua University
Guangzhou University

Type

Article

Journal

Reliability Engineering and System Safety

Volume

242

No. of pages

19

ISSN

0951-8320

Publication date

02.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality, Industrial and Manufacturing Engineering

Electronic version(s)

https://doi.org/10.1016/j.ress.2023.109695 (Access: Closed)