Parameter estimation for load‐sharing system subject to Wiener degradation process using the expectation‐maximization algorithm

In practice, many systems exhibit load‐sharing behavior, where the surviving components share the total load imposed on the system. Different from general systems, the components of load‐sharing systems are interdependent in nature, in such a way that when one component fails, the system load has to...

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Vydané v:Quality and reliability engineering international Ročník 35; číslo 4; s. 1010 - 1024
Hlavní autori: Xu, Jianyu, Liu, Bin, Zhao, Xiujie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 01.06.2019
Predmet:
Algorithms
Component reliability
continuous degradation
Degradation
EM algorithm
Estimating techniques
Failure rates
load‐sharing system
Maximization
Maximum likelihood estimators
Optimization
Parameter estimation
Reliability analysis
Wiener degradation
ISSN:0748-8017, 1099-1638
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Shrnutí:In practice, many systems exhibit load‐sharing behavior, where the surviving components share the total load imposed on the system. Different from general systems, the components of load‐sharing systems are interdependent in nature, in such a way that when one component fails, the system load has to be shared by the remaining components, which increases the failure rate or degradation rate of the remaining components. Because of the load‐sharing mechanism among components, parameter estimation and reliability assessment are usually complicated for load‐sharing systems. Although load‐sharing systems with components subject to sudden failures have been intensely studied in literatures with detailed estimation and analysis approaches, those with components subject to degradation are rarely investigated. In this paper, we propose the parameter estimation method for load‐sharing systems subject to continuous degradation with a constant load. Likelihood function based on the degradation data of components is established as a first step. The maximum likelihood estimators for unknown parameters are deduced and obtained via expectation‐maximization (EM) algorithm considering the nonclosed form of the likelihood function. Numerical examples are used to illustrate the effectiveness of the proposed method.
Bibliografia:ObjectType-Article-1
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ISSN:0748-8017
1099-1638
DOI:10.1002/qre.2442