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机器学习应用于随机退化设备剩余寿命预测的综述

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为了研究机器学习在设备剩余寿命(remaining useful life,RUL)预测领域发挥的作用及面临的挑战,对机器学习在设备剩余寿命预测领域的应用方法研究及每类方法特点开展系统性总结.根据模型训练方式以及有无标签的差异,将常见机器学习应用于随机退化设备剩余寿命预测的研究分为监督学习、无监督学习、半监督学习、迁移学习及强化学习 5 类,综述了每类方法在设备剩余寿命预测领域的应用现状;列举了多种方法优势结合、相互弥补的典型案例,并阐述了不同方法在预测中所起到的作用;简要介绍了各类方法的特点及应用领域,并分析了不同方法的优势与缺陷;着眼设备运行过程中所面临的现实问题和需求,探讨了随机退化设备剩余寿命预测未来所面临的挑战与难题.
A review of remaining useful life prediction for stochastic degrading devices based on machine learning
Aiming to explore the role and challenges of machine learning in predicting the remaining useful life of devices,this study conducted a systematic summary of machine learning in RUL prediction of devices in terms of application methods and characteristics of each category of methods.The research on the application of common machine learning to RUL prediction of stochastic degrading devices is divided into five categories according to the difference in model training methods and whether there is a label or not:supervised learning,unsupervised learn-ing,semisupervised learning,transfer learning,and reinforcement learning.The current applications of each cate-gory of methods in the field of RUL prediction are summarized.Typical cases are listed,where the advantages of various methods are combined and complement each other,and the roles of these different methods in the prediction are explained.This study also briefly introduces the characteristics and application fields of each method and analy-zes the advantages and shortcomings of different methods.The practical problems and requirements during equip-ment operation are focused on,and the challenges and difficulties of RUL prediction for stochastic degrading de-vices are discussed.

remaining service life predictionstochastic degrading devicemachine learningsupervised learningunsupervised learningsemisupervised learningtransfer learningreinforcement learning

张波、胡昌华、张浩、郑建飞、张建勋、牟含笑

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火箭军工程大学 导弹工程学院,陕西 西安 710025

西安航天精密机电研究所,陕西 西安 710100

剩余寿命预测 随机退化设备 机器学习 监督学习 无监督学习 半监督学习 迁移学习 强化学习

国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目

622278146210343362373368

2024

10.11990/jheu.202305028

哈尔滨工程大学学报
哈尔滨工程大学

哈尔滨工程大学学报

CSTPCD北大核心
影响因子：0.655
ISSN：1006-7043
年,卷(期)：2024.45(9)