期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 10, 页码 5480-5491出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3070840
关键词
Degradation; Machinery; Feature extraction; Task analysis; Prognostics and health management; Condition monitoring; Training; Cycle-consistent learning; deep learning; degradation alignment; prognostics; remaining useful life (RUL) prediction
类别
资金
- China Postdoctoral Science Foundation [2019M651032]
- National Natural Science Foundation of China [52005086, 11902202]
- Liaoning Provincial Department of Science and Technology [2020-BS-048, 2019-BS-184]
- Fundamental Research Funds for the Central Universities [N2005010, N180708009]
- Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education, Northeastern University [VCAME201906]
This paper proposes a deep learning-based RUL prediction method, which aligns the data of different entities in similar degradation levels through a cycle-consistent learning scheme to improve prediction performance. Experimental results suggest that the method offers a novel perspective on RUL estimations.
Due to the benefits of reduced maintenance cost and increased operational safety, effective prognostic methods have always been highly demanded in real industries. In the recent years, intelligent data-driven remaining useful life (RUL) prediction approaches have been successfully developed and achieved promising performance. However, the existing methods mostly set hard RUL labels on the training data and pay less attention to the degradation pattern variations of different entities. This article proposes a deep learning-based RUL prediction method. The cycle-consistent learning scheme is proposed to achieve a new representation space, where the data of different entities in similar degradation levels can be well aligned. A first predicting time determination approach is further proposed, which facilitates the following degradation percentage estimation and RUL prediction tasks. The experimental results on a popular degradation data set suggest that the proposed method offers a novel perspective on data-driven prognostic studies and a promising tool for RUL estimations.
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