Classical and Bayesian estimation of stress-strength reliability of a component having multiple states

Siju, KC, Kumar, Mahesh and Beer, Michael ORCID: 0000-0002-0611-0345 (2021) Classical and Bayesian estimation of stress-strength reliability of a component having multiple states. INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT, 38 (2). pp. 528-535.

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Official URL: http://dx.doi.org/10.1108/ijqrm-01-2020-0009

Abstract

<jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>This article presents the multi-state stress-strength reliability computation of a component having three states namely, working, deteriorating and failed state.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>The probabilistic approach is used to obtain the reliability expression by considering the difference between the values of stress and strength of a component, say, for example, the stress (load) and strength of a power generating unit is in terms of megawatt. The range of values taken by the difference variable determines the various states of the component. The method of maximum likelihood and Bayesian estimation is used to obtain the estimators of the parameters and system reliability.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title><jats:p>The maximum likelihood and Bayesian estimates of the reliability approach the actual reliability for increasing sample size.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Originality/value</jats:title><jats:p>Obtained a new expression for the multi-state stress-strength reliability of a component and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability.</jats:p></jats:sec>

Item Type:	Article
Uncontrolled Keywords:	Multi-state, Stress-strength reliability, Maximum likelihood estimation, Bayesian estimation, Gibbs sampling
Depositing User:	Symplectic Admin
Date Deposited:	14 Jul 2020 09:01
Last Modified:	18 Jan 2023 23:46
DOI:	10.1108/IJQRM-01-2020-0009
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3093826