Prognostic models based on statistical flowgraphs

被引：7
作者：
Collins, David H. [1 ]
Huzurbazar, Aparna V. [1 ]
机构：
[1] Los Alamos Natl Lab, Stat Sci Grp, Los Alamos, NM 87545 USA
来源：
APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 2012年 / 28卷 / 02期
关键词：
prognostics and health management; hierarchical modeling; semi-Markov process; empirical transform; Laplace transform inversion; MULTISTATE MODELS; FATIGUE; FAILURE;
D O I：
10.1002/asmb.884
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
We present a framework for developing hierarchical models for predicting system health (e.g. probability of failure within a given mission duration), based on component-level reliability and degradation models. Component models may be specified as parametric probability distributions or nonparametrically as empirical distribution functions. Flowgraph methods are then used to predict the system failure time distribution. We illustrate with an application to aircraft maintenance. Copyright (c) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码：141 / 151
页数：11
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