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

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL Reliability Engineering & System Safety Pub Date : 2024-10-05 DOI:10.1016/j.ress.2024.110549
Gyeongho Kim , Yun Seok Kang , Sang Min Yang , Jae Gyeong Choi , Gahyun Hwang , Hyung Wook Park , Sunghoon Lim
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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.
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在不同工作条件下预测加工工具剩余使用寿命的渔业信息持续学习方法
准确预测设备的剩余使用寿命(RUL)已成为制造业的一项重要任务。它不仅有助于防止意外故障,还能最大限度地利用可用寿命,从而提高流程效率。然而,在实践中,使用随时间变化的多种运行条件阻碍了高效的数据驱动型 RUL 预测。与传统的监督学习设置不同,不同的运行条件会产生具有时变生成分布的异构数据。因此,由于建模和内存成本增加,现有方法无法有效应用。切削加工是受这一问题困扰的领域之一,切削工具的 RUL 预测对生产率至关重要。考虑到工作条件不断变化的实际情况,本研究提出了一种名为费舍尔信息持续学习(FICL)的方法,该方法可实现高效的工具 RUL 预测,并能随着条件的变化进行自适应学习,而无需存储以前的数据和模型。FICL 利用费雪信息,通过锐化最小化提高泛化能力,并通过结构正则化在工作条件之间传递知识。使用真实世界中五种不同工作条件下的加工过程数据集进行的实验证明了 FICL 的功效,表明其在所有工作条件下的预测性能均优于现有方法。特别是,FICL 的灾难性遗忘最少,这意味着它能有效保留不同工作条件下的信息知识。
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
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.
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