Gyeongho Kim , Yun Seok Kang , Sang Min Yang , Jae Gyeong Choi , Gahyun Hwang , Hyung Wook Park , Sunghoon Lim
{"title":"Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions","authors":"Gyeongho Kim , Yun Seok Kang , Sang Min Yang , Jae Gyeong Choi , Gahyun Hwang , Hyung Wook Park , Sunghoon Lim","doi":"10.1016/j.ress.2024.110549","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832024006215","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.