编号：CDUT-2023-6

标题：Time series chain graph for modeling reliability covariates in degradation process

入藏号：WOS:000583913400064

中国科学院文献情报中心期刊分区：工程技术1区/TOP（2020年升级版）

本校作者： 徐湖洋

来源出版物：RELIABILITY ENGINEERING & SYSTEM SAFETY  卷: 204  文献号: 107207

出版年：2020

第一地址： 成都理工大学

关键词： Reliability covariate model; Time series chain graph; Degradation process; Remaining useful life prediction

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摘要：In product health management, degradation modeling methods have been recognized as essential and effective for the lifetime and remaining useful life (RUL) estimations. In many applications, covariate-related data provided by product users can be regarded as fragments of life-cycle records. For a particular fragment, it is possible to suggest several possible degradation conditions simultaneously. These degradation conditions may lead to different results of the RUL estimation. One way to solve such a problem is to increase the life-cycle degradation model's screening capacity of degradation conditions. In this paper, time series chain graph (TSCG), which could effectively determine the possible degradation conditions by modeling the dependencies between time-varying risk factors and performance measurements, is proposed. The procedures of model construction based on observed time series and the use of the proposed model for RUL prediction are given. Based on the inherent complexity of the TSCG structure, it is possible to distinguish the degradation conditions better so that RUL's identification is more reliable. Finally, the validity of the proposed model is illustrated by a turbofan engine degradation case study, which consists of the time series for engine operation and degradation process.

文章链接地址: https://www.sciencedirect.com/science/article/pii/S0951832020307080