TY - GEN
T1 - Problem Decoupling and Optimization of Aeroengine Life Cycle Maintenance Decision
AU - Feng, Yalong
AU - Fu, Xuyun
AU - Wang, Lijun
AU - Bai, Zhengfeng
AU - Wang, Rui
AU - Chen, Haibo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Reasonable life cycle maintenance decision of aeroengine, determining the aeroengine maintenance interval and maintenance workscope, can effectively reduce aeroengine maintenance costs. To achieve this, an optimization model of maintenance decision of aeroengine life cycle is established, taking the lowest total maintenance cost in the life cycle as the optimization objective, and using the aeroengine life cycle maintenance interval and maintenance workscope as decision variables. In order to reduce the size of the model's solution space, the maintenance interval and the maintenance workscope are decoupled, and the optimal maintenance strategy to determine the maintenance workscope is proposed. Subsequently, particle swarm optimization algorithm is used to search the global optimal solution of the model. Finally, the effectiveness of the model is evaluated according to relevant numerical experiments and real aeroengine data. The results show that a better solution can be obtained in a short time for problems within 30 life limited parts, 28 modules and 90000 flight cycles.
AB - Reasonable life cycle maintenance decision of aeroengine, determining the aeroengine maintenance interval and maintenance workscope, can effectively reduce aeroengine maintenance costs. To achieve this, an optimization model of maintenance decision of aeroengine life cycle is established, taking the lowest total maintenance cost in the life cycle as the optimization objective, and using the aeroengine life cycle maintenance interval and maintenance workscope as decision variables. In order to reduce the size of the model's solution space, the maintenance interval and the maintenance workscope are decoupled, and the optimal maintenance strategy to determine the maintenance workscope is proposed. Subsequently, particle swarm optimization algorithm is used to search the global optimal solution of the model. Finally, the effectiveness of the model is evaluated according to relevant numerical experiments and real aeroengine data. The results show that a better solution can be obtained in a short time for problems within 30 life limited parts, 28 modules and 90000 flight cycles.
KW - aeroengine
KW - life cycle
KW - life limited part
KW - maintenance interval
KW - maintenance workscope
KW - module
UR - https://www.scopus.com/pages/publications/85165066388
U2 - 10.1109/PHM58589.2023.00015
DO - 10.1109/PHM58589.2023.00015
M3 - 会议稿件
AN - SCOPUS:85165066388
T3 - Proceedings - 2023 Prognostics and Health Management Conference - Paris, PHM-Paris 2023
SP - 34
EP - 41
BT - Proceedings - 2023 Prognostics and Health Management Conference - Paris, PHM-Paris 2023
A2 - Li, Chuan
A2 - Valentino, Gianluca
A2 - Huang, Weilin
A2 - Pu, Zhiqiang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Prognostics and Health Management Conference - Paris, PHM-Paris 2023
Y2 - 31 May 2023 through 2 June 2023
ER -