Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions

• Published in: Reliability engineering & system safety 2025-01, Vol.253, p.110549, Article 110549
• Main Authors: Kim, Gyeongho, Kang, Yun Seok, Yang, Sang Min, Choi, Jae Gyeong, Hwang, Gahyun, Park, Hyung Wook, Lim, Sunghoon
• Format: Article
• Language: English
• Subjects: Continual learning; Deep learning; Machining tools; Prognostics and health management; Remaining useful life; Varying operating conditions
• ISSN: 0951-8320
• DOI: 10.1016/j.ress.2024.110549

Accurate prediction of remaining useful life (RUL) of equipment has become an essential task in manufacturing. It not only helps prevent unexpected failures but also enables maximal utilization of available life, thus improving process efficiency. In practice, however, the use of multiple operating conditions that vary by time impedes efficient data-driven RUL prediction. Unlike conventional supervised learning setups, varying operating conditions generate heterogeneous data with time-varying generating distributions. Thus, existing approaches cannot be effectively applied due to increasing modeling and memory costs. One of the domains that suffer from this issue is machining, where RUL prediction of cutting tools is crucial for productivity. Considering realistic circumstances with varying operating conditions, this work proposes a method named Fisher-informed continual learning (FICL), which enables efficient tool RUL prediction that adaptively learns as conditions change without storing previous data and models. FICL uses Fisher information to improve generalization via sharpness-aware minimization and transfer knowledge between operating conditions through structural regularization. Experiments using datasets from real-world machining processes under five distinct operating conditions prove FICL’s efficacy, indicating its superior prediction performance to existing methods for all operating conditions. Particularly, FICL manifests the least catastrophic forgetting, implying it effectively retains informative knowledge from varying operating conditions. •A remaining useful life prediction is performed for varying operating conditions.•Fisher-informed sharpness-aware minimization is used for generalization performance.•Fisher-informed structural regularization helps to retain knowledge continually.•Experiments show proposed method’s advantage to existing methods in continual setup.•The proposed method overcomes existing works’ limitations in continual learning.