Selective maintenance optimization with stochastic break duration based
on reinforcement learning
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Online publication date: 2022-10-24
Publication date: 2022-10-24
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(4):771–784
HIGHLIGHTS
- Selective maintenance model with stochastic break duration is proposed.
- Reinforcement learning(RL) method is applied to selective maintenance model.
- The advantages of considering stochastic break duration and RL are analysed.
KEYWORDS
ABSTRACT
For industrial and military applications, a sequence of missions would be performed with
a limited break between two adjacent missions. To improve the system reliability, selective
maintenance may be performed on components during the break. Most studies on selective
maintenance generally use minimal repair and replacement as maintenance actions while
break duration is assumed to be deterministic. However, in practical engineering, many
maintenance actions are imperfect maintenance, and the break duration is stochastic due to
environmental and other factors. Therefore, a selective maintenance optimization model is
proposed with imperfect maintenance for stochastic break duration. The model is aimed to
maximize the reliability of system successfully completing the next mission. The reinforcement learning(RL) method is applied to optimally select maintenance actions for selected
components. The proposed model and the advantages of the RL are verified by three case
studies verify.
CITATIONS (1):
1.
Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization
Oluwaseyi Ogunfowora, Homayoun Najjaran
Journal of Manufacturing Systems