TY - GEN
T1 - Fleet-Level Multi-objective Selective Maintenance Method Considering Mission Requirement and System Reliability
AU - Wang, Pengxiang
AU - Liu, Shulin
AU - Yi, Xiaojian
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2025.
PY - 2025
Y1 - 2025
N2 - The performance of fleet in the mission depends on mission requirements and the health states of all individuals. Addressing the maintenance decision-making problem for equipment fleet executing continuous missions, an improved fleet-level multi-objective selective maintenance planning method is proposed that considers mission requirements and system reliability. The method uses a virtual age model to describe the lifespan of components and employs conditional survival probability to predict the performance of equipment in a given health state during missions. A relationship between fleet mission capability and equipment health states is established through universal generating function. The optimization model aims to minimize maintenance costs and maximize the probability that the fleet meets mission requirements. The model is optimized using the NSGA-III algorithm, and the advantages of this solution method are demonstrated through comparisons with other algorithms.
AB - The performance of fleet in the mission depends on mission requirements and the health states of all individuals. Addressing the maintenance decision-making problem for equipment fleet executing continuous missions, an improved fleet-level multi-objective selective maintenance planning method is proposed that considers mission requirements and system reliability. The method uses a virtual age model to describe the lifespan of components and employs conditional survival probability to predict the performance of equipment in a given health state during missions. A relationship between fleet mission capability and equipment health states is established through universal generating function. The optimization model aims to minimize maintenance costs and maximize the probability that the fleet meets mission requirements. The model is optimized using the NSGA-III algorithm, and the advantages of this solution method are demonstrated through comparisons with other algorithms.
KW - Fleet-level maintenance
KW - Maintenance planning
KW - Multiobjective optimization
KW - Non-dominated sorting algorithm
UR - http://www.scopus.com/inward/record.url?scp=105002584113&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3576-4_38
DO - 10.1007/978-981-96-3576-4_38
M3 - Conference contribution
AN - SCOPUS:105002584113
SN - 9789819635757
T3 - Lecture Notes in Electrical Engineering
SP - 426
EP - 434
BT - Proceedings of 4th 2024 International Conference on Autonomous Unmanned Systems, 4th ICAUS 2024 - Volume VI
A2 - Liu, Lianqing
A2 - Niu, Yifeng
A2 - Fu, Wenxing
A2 - Qu, Yi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Autonomous Unmanned Systems, ICAUS 2024
Y2 - 19 September 2024 through 21 September 2024
ER -