Sensitivity Estimation of Markov Reward Models and Its Applications to Component Importance Analysis

Junjun Zheng (), Hiroyuki Okamura and Tadashi Dohi
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Junjun Zheng: Osaka University
Hiroyuki Okamura: Hiroshima University
Tadashi Dohi: Hiroshima University

A chapter in Advances in Reliability and Maintainability Methods and Engineering Applications, 2023, pp 103-132 from Springer

Abstract: Abstract Component importance analysis measures the effect on system reliability of components’ reliabilities, enables the analyst to rank each component’s contribution to the system failure, and identifies the system’s weak components. Thus the system reliability can be improved by upgrading the weak components. Component importance analysis is commonly used in the design of a system from the reliability point of view. However, although dependencies exist among the failure behavior of systems in practice, and the dependent failures are known as a risk factor for degradation of system reliability, it is difficult to evaluate the component importance measures in the presence of failure dependencies analytically. In this chapter, we consider the Markov chain-based component-wise sensitivity analysis, which can evaluate the component importance measures without any system structure function. In particular, three types of component importance measures are derived from the viewpoints of both steady-state availability and reliability. Also, numerical examples illustrate the component importance analysis with the proposed approach.

Keywords: Markov reward model; Failure dependency; Parametric sensitivity; Component importance analysis; Birnbaum importance; Criticality importance; Upgrading function (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-28859-3_5

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