@inproceedings{2050657c82a0409fbea183d0e28fb052,
title = "Maintenance Decision Making using State Dependent Markov Analysis with Failure Couplings",
abstract = "The maintenance decision making considering various types of coupling effect within system components has been a focus in the field of operation and maintenance (O and M) due to its wide applications in engineering systems with potentially significant economic benefits. Various opportunities as the coupling effect can provide, it leads to the difficulty in system modeling and policy establishment when multiple types of dependence exist in the system. This paper presents a novel approach to reflect economic dependence and stochastic dependence in the Markov model so that an optimal maintenance policy can be selected through policy performance comparison. Failure rates of system components change according to the health states of related components and combined maintenance actions introduce O and M time and cost savings. A case study has been used to demonstrate the developed method and further illustrate the benefit of combined maintenance actions in different O and M scenarios considering failure couplings among system components.",
keywords = "Markov Model, coupling effect, failure rate, maintenance",
author = "Xinyang Liu and Pingfeng Wang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020 ; Conference date: 20-08-2020 Through 23-08-2020",
year = "2020",
month = aug,
doi = "10.1109/APARM49247.2020.9209324",
language = "English (US)",
series = "2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020",
address = "United States",
}