Self-supervised Health Representation Decomposition based on contrast learning

Yilin Wang, Lei Shen, Yuxuan Zhang, Yuanxiang Li, Ruixin Zhang and Yongshen Yang

Reliability Engineering and System Safety, 2023, vol. 239, issue C

Abstract: Accurately predicting the Remaining Useful Life (RUL) of equipment and diagnosing faults (FD) in Prognostics and Health Management (PHM) applications requires effective feature engineering. However, the large amount of time series data now available in industry is often unlabeled and contaminated by variable working conditions and noise, making it challenging for traditional feature engineering methods to extract meaningful system state representations from raw data. To address this issue, this paper presents a Self-supervised Health Representation Decomposition Learning(SHRDL) framework that is based on contrast learning. To extract effective representations from raw data with variable working conditions and noise, SHRDL incorporates an Attention-based Decomposition Network (ADN) as its encoder. During the contrast learning process, we incorporate cycle information as a priori and define a new loss function, the Cycle Information Modified Contrastive loss (CIMCL), which helps the model focus more on the contrast between hard samples. We evaluated SHRDL on three popular PHM datasets (N-CMAPPS engine dataset, NASA, and CALCE battery datasets) and found that it significantly improved RUL prediction and FD performance. Experimental results demonstrate that SHRDL can learn health representations from unlabeled data under variable working conditions and is robust to noise interference.

Keywords: Prognostics and Health Management; Self-supervised learning; Representation learning; Remaining Useful Life Prediction; Fault Diagnosis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023003691

DOI: 10.1016/j.ress.2023.109455

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