Subset simulation for optimal sensors positioning based on value of information

Seyed Mojtaba Hoseyni, Francesco Di Maio and Enrico Zio

Journal of Risk and Reliability, 2023, vol. 237, issue 5, 897-909

Abstract: Greedy and non-greedy optimization methods have been proposed for maximizing the Value of Information (VoI) for equipment health monitoring by optimal sensors positioning. These methods provide good solutions, but still with limitations and challenges: greedy optimization does not guarantee to find the optimal solution, due to the non-submodularity of the VoI; non-greedy optimization does not suffer from the non-submodularity of the VoI but requires computationally expensive and tedious simulations to find the optimal solution. In this work, the Subset Simulation (SS) method is originally proposed to address these limitations and challenges. A real case study is considered concerning the condition monitoring of a Steam Generator (SG) of a Prototype Fast Breeder Reactor (PFBR). Results show that SS, even if initialized with a small number of Monte Carlo samples, is capable of finding the optimal set of sensors positions in a very short computational time and is insensitive to the non-submodularity of VoI.

Keywords: Health monitoring; sensors positioning; value of information; optimization; greedy; genetic algorithm; particle swarm optimization; subset simulation; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:237:y:2023:i:5:p:897-909

DOI: 10.1177/1748006X221118432

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