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Reliability analysis and emergency maintenance strategy optimization of the multi-state airborne power supply system based on dependent competitive importance measure

Author

Listed:
  • Jing, Yongfeng
  • Jiao, Jian
  • Yuan, Yuan
  • Chen, Zhiwei
  • Lu, Chen
  • Dui, Hongyan

Abstract

The airborne power supply system provides reliable and stable power supply for airborne equipment, and its performance directly impacts mission success and flight safety. However, the airborne power supply system faces numerous challenges, particularly from environmental interference and internal components ageing, which create significant effects on maintaining stable operation during flights. Due to these complex factors, reliability analysis and optimization of emergency maintenance strategies for the system is difficult and rarely studied. In this paper, we propose a multi-state system reliability analysis and emergency maintenance strategy optimization method based on the dependent competing importance measure (DCIM). Firstly, a dependent competing failure process model with variable degradation rates is established to address the dynamic evolution of component states. Secondly, a system reliability analysis model based on the dependent competing failure process model and the semi-Markov process is developed to improve the accuracy of reliability assessment. Thirdly, the emergency maintenance strategy based on the DCIM is proposed and optimized under budgetary constraints. Finally, an airborne power supply system serves as case study to validate the efficacy of the proposed method and provide practical guidance for emergency maintenance decisions in similar applications.

Suggested Citation

  • Jing, Yongfeng & Jiao, Jian & Yuan, Yuan & Chen, Zhiwei & Lu, Chen & Dui, Hongyan, 2025. "Reliability analysis and emergency maintenance strategy optimization of the multi-state airborne power supply system based on dependent competitive importance measure," Reliability Engineering and System Safety, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:reensy:v:263:y:2025:i:c:s0951832025004752
    DOI: 10.1016/j.ress.2025.111274
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