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Probabilistic risk assessment of civil aircraft associated failures under condition-based maintenance

Author

Listed:
  • Guo, Yuanyuan
  • Sun, Youchao
  • Si, Qingmin
  • Guo, Xinyao
  • Chen, Nongtian

Abstract

Maintenance can improve an aircraft system's reliability over a long operation period or when a component has failed. However, inappropriate maintenance inspection intervals will cause latent failures to be covered or undetected, leading to a large number of unplanned flight disruptions for airlines. In this paper, we present a two-stage framework to assess the associated failure risk of civil aircraft under condition-based maintenance. In the first stage of the framework, the probability of primary functional failure across the lifecycles of the monitored component is determined by analyzing whether the current inspection interval prevents the component from progressing from latent failure to functional failure. In the second stage of the framework, the associated failure probability between components and related systems is formulated by the adjacency matrix. The structure and performance of the proposed model were tested on a case study by run-to-failure data associated with aircraft engines from a large airline. Focusing on the scenario of turbine disk cracking leading to fragment penetration of the fuel tank and causing fire as the consequential fault impact path, the results show that the risk of aircraft fire caused by turbine disk fragments falls within an acceptable range, necessitating the completion of inspections and subsequent monitoring within the stipulated timeframe. The method can be used to readjust the inspection interval, optimize the operation plan, improve the on-time performance of flights, and reduce the risk of aviation accidents.

Suggested Citation

  • Guo, Yuanyuan & Sun, Youchao & Si, Qingmin & Guo, Xinyao & Chen, Nongtian, 2025. "Probabilistic risk assessment of civil aircraft associated failures under condition-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006227
    DOI: 10.1016/j.ress.2024.110550
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