TY - GEN
T1 - Selective maintenance of multi-component systems with multiple failure modes
AU - Ruiz, Cesar
AU - Pohl, Edward
AU - Liao, Haitao
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - In this paper, we develop a novel selective maintenance framework for optimizing the reliability or economic performance of a multi-component system subject to multiple failure modes. We classify the failure modes into soft failures and hard failures (possibly dependent). Soft failures are defined as occurring when a degradation process under condition monitoring surpasses a predefined threshold while hard failures are spontaneous failures that cannot be monitored. To optimize the system performance, different levels of maintenance actions on multiple components are considered. In particular, imperfect maintenance actions reduce the effective age and degradation level of a component while increasing the component's frailty. We solve the optimization problem using standard Differential Evolution (DE) and Genetic Algorithm (GA) heuristics. A numerical example shows that the proposed selective maintenance framework provides an effective tool for making the optimal maintenance decisions by considering hard failures and the correlation among degradation processes.
AB - In this paper, we develop a novel selective maintenance framework for optimizing the reliability or economic performance of a multi-component system subject to multiple failure modes. We classify the failure modes into soft failures and hard failures (possibly dependent). Soft failures are defined as occurring when a degradation process under condition monitoring surpasses a predefined threshold while hard failures are spontaneous failures that cannot be monitored. To optimize the system performance, different levels of maintenance actions on multiple components are considered. In particular, imperfect maintenance actions reduce the effective age and degradation level of a component while increasing the component's frailty. We solve the optimization problem using standard Differential Evolution (DE) and Genetic Algorithm (GA) heuristics. A numerical example shows that the proposed selective maintenance framework provides an effective tool for making the optimal maintenance decisions by considering hard failures and the correlation among degradation processes.
KW - Degradation Modelling
KW - Heuristic Optimization
KW - Selective Maintenance
UR - http://www.scopus.com/inward/record.url?scp=85088259707&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088259707&partnerID=8YFLogxK
U2 - 10.1109/RAMS48030.2020.9153707
DO - 10.1109/RAMS48030.2020.9153707
M3 - Conference contribution
AN - SCOPUS:85088259707
T3 - Proceedings - Annual Reliability and Maintainability Symposium
BT - 2020 Annual Reliability and Maintainability Symposium, RAMS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Annual Reliability and Maintainability Symposium, RAMS 2020
Y2 - 27 January 2020 through 30 January 2020
ER -