A reinforcement learning approach to optimal part flow management for gas turbine maintenance

Michele Compare, Luca Bellani, Enrico Cobelli, Enrico Zio, Francesco Annunziata, Fausto Carlevaro and Marzia Sepe

Journal of Risk and Reliability, 2020, vol. 234, issue 1, 52-62

Abstract: We consider the maintenance process of gas turbines used in the Oil and Gas industry: the capital parts are first removed from the gas turbines and replaced by parts of the same type taken from the warehouse; then, they are repaired at the workshop and returned to the warehouse for use in future maintenance events. Experience-based rules are used to manage the flow of the parts for a profitable gas turbine operation. In this article, we formalize the part flow management as a sequential decision problem and propose reinforcement learning for its solution. An application to a scaled-down case study derived from real industrial practice shows that reinforcement learning can find policies outperforming those based on experience-based rules.

Keywords: Part flow; reinforcement learning; gas turbine (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:234:y:2020:i:1:p:52-62

DOI: 10.1177/1748006X19869750

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