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What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance
Author(s) -
Li Shuo,
Pozzi Matteo
Publication year - 2019
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2329
Subject(s) - value of information , computer science , risk analysis (engineering) , predictability , markov decision process , process (computing) , metric (unit) , key (lock) , parametric statistics , reliability engineering , operations research , markov process , engineering , computer security , operations management , artificial intelligence , business , statistics , physics , mathematics , quantum mechanics , operating system
Summary Information collected by monitoring systems can provide a significant economic benefit to the operation and maintenance of infrastructure components only under specific conditions. The information has to be precise, not redundant, related to relevant decision problems under uncertainty as, for example, the appropriate scheduling of maintenance actions, and the decision maker needs to be able to process that information and react timely. All these considerations can be naturally embedded in the value of information (VoI), a utility‐based metric for assessing the impact of the additional information in decision making under uncertainty. In this paper, we investigate the relation between the VoI and key features of the monitoring system, of the component deterioration and of the decision‐making process, including measure accuracy and availability, deterioration rates, damage predictability, reaction time, maintenance costs, and the economic discount factor. By leveraging previous work, we model the maintenance process as a partially observable Markov decision process, and we compute the VoI of long‐term monitoring. Our proposed framework allows for a detailed quantitative analysis on the joint effects of these features and can be useful to identify conditions when the benefit of monitoring is high, to assign priorities among components that deserve to be instrumented or to optimize the allocation of resources to monitoring efforts.