期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 133, 期 -, 页码 266-274出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2014.08.013
关键词
Condition monitoring; Prognostics; Functional principal components analysis; Functional regression analysis; Remaining useful life
资金
- USA National Science Foundation (NSF) [CMMI-1200639]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1200639] Funding Source: National Science Foundation
Most prognostic degradation models rely on a relatively accurate and comprehensive database of historical degradation signals. Typically, these signals are used to identify suitable degradation trends that are useful for predicting lifetime. In many real-world applications, these degradation signals are usually incomplete, i.e., contain missing observations. Often the amount of missing data compromises the ability to identify a suitable parametric degradation model. This paper addresses this problem by developing a semi-parametric approach that can be used to predict the remaining lifetime of partially degraded systems. First, key signal features are identified by applying Functional Principal Components Analysis (FPCA) to the available historical data. Next, an adaptive functional regression model is used to model the extracted signal features and the corresponding times-to-failure. The model is then used to predict remaining lifetimes and to update these predictions using real-time signals observed from fielded components. Results show that the proposed approach is relatively robust to significant levels of missing data. The performance of the model is evaluated and shown to provide significantly accurate predictions of residual lifetime using two case studies. (C) 2014 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据