中文摘要：

寿命预测和健康管理技术是可靠性工程领域的核心技术之一,在过去的几十年里得到了蓬勃的发展和广泛的应用.由于难以获取复杂、高可靠性设备失效机理的物理模型,数据驱动的预测方法成为近年来研究的热点.在数据驱动的预测技术中,数据缺失、设备失效过程的不确定性等问题成为制约预测准确性的重要因素.本文在基于失效数据、基于退化数据和多源数据融合的分类框架下,对寿命预测技术进行了综述,特别关注了基于退化数据的预测方法.通过介绍状态监测、维修决策和备件订购三个热点研究问题,概述了基于寿命预测结果的健康管理技术最新研究进展.最后探讨了该领域亟需解决的问题和未来可能的研究方向.

英文摘要：

Life prediction and health management are core technologies in the reliability engineering field and have been widely used in the past decade.Since it is difficult to obtain the physical failure mechanisms of complex equipment with high reliability,data-driven prediction methods have become mainstream techniques in recent years.In data-driven prediction technology,lack of data,uncertainty of equipment failure processes,and other issues have been identified as important factors that influence prediction accuracy.In view of these problems and the deficiencies of previous studies,a large number of authors have proposed improvement measures.In this paper,under the classifications of failure data-based,degradation data-based,and multisource data fusion,we review life prediction technology with an emphasis on degradation data-based prediction methods.We list the problems that have been solved and their corresponding improvement measures.In addition,we introduce the three hotspot issues of condition monitoring,maintenance policy,and spare ordering,and summarize new advances in health management techniques based on life prediction results.Finally,we highlight the urgent problems and promising research directions that deserve further research.