Optimal inspection of binary systems via Value of Information analysis

Chaochao Lin, Junho Song and Matteo Pozzi

Reliability Engineering and System Safety, 2022, vol. 217, issue C

Abstract: We develop computable metrics to assign priorities for information collection on binary systems composed of binary components. Components are worth inspecting because their condition states are uncertain, and system functioning depends on them. The Value of Information (VoI) enables assessment of the impact of information in decision making under uncertainty, including the component’s reliability and role in the system, the precision of the observation, the available maintenance actions and the expected economic loss. We introduce the VoI-based metrics for system-level (“global†) and component-level (“local†) maintenance actions, analyze the properties of these metrics, and apply them to series and parallel systems. We discuss their computational complexity in applications to general network systems and, to tame the complexity for the local metric assessment, we present a heuristic and assess its performance on some case studies.

Keywords: Binary networks; Importance Measure; Inspections; Value of Information (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:217:y:2022:i:c:s0951832021004555

DOI: 10.1016/j.ress.2021.107944

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