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Application of Extended Cox Regression Model to Time-On-Wing Data of Aircraft Repairables

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  • Thijssens, O.W.M.
  • Verhagen, Wim J.C.

Abstract

Global and local aviation traffic is growing while economic and performance pressures on the industry are increasing. As a consequence, airlines try to maximise their fleet utilization. Airline operators and Maintenance, Repair and Overhaul (MRO) providers therefore require as much insight as possible in factors affecting component reliability and availability. Reliability analysis in literature rarely considers the existence of relations between explanatory variables and time-based component reliability, and includes strict assumptions on independence of events and underlying distributions. This disregards the complex nature of aircraft operations, where the probability of an event may be influenced by various operational and maintenance factors. This paper develops new insights from operational and maintenance data about the impact of operating environment and ageing of components and fleet on reliability of the components by incorporating these factors in an extension of the Cox regression model. Examination of results obtained from analysing historical data of a set of three components with respect to installations and removals indicate that the natural environment at the hub airport, maintenance history of components, the age of the aircraft on which the component is installed and different modification designs are useful significant predictors of the time-on-wing duration of the component.

Suggested Citation

  • Thijssens, O.W.M. & Verhagen, Wim J.C., 2020. "Application of Extended Cox Regression Model to Time-On-Wing Data of Aircraft Repairables," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306372
    DOI: 10.1016/j.ress.2020.107136
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    References listed on IDEAS

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    1. Louit, D.M. & Pascual, R. & Jardine, A.K.S., 2009. "A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1618-1628.
    2. Kilpi, Jani & Vepsäläinen, Ari P.J., 2004. "Pooling of spare components between airlines," Journal of Air Transport Management, Elsevier, vol. 10(2), pages 137-146.
    3. Patrick Royston, 2015. "Tools for checking calibration of a Cox model in external validation: Prediction of population-averaged survival curves based on risk groups," Stata Journal, StataCorp LP, vol. 15(1), pages 275-291, March.
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    Cited by:

    1. Kim, Myeonghyeon & Bae, Jiheon, 2021. "Modeling the flight departure delay using survival analysis in South Korea," Journal of Air Transport Management, Elsevier, vol. 91(C).
    2. Hu, Wei & Yang, Zhaojun & Chen, Chuanhai & Wu, Yue & Xie, Qunya, 2021. "A Weibull-based recurrent regression model for repairable systems considering double effects of operation and maintenance: A case study of machine tools," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Zhu, Xiaojun & Balakrishnan, N., 2022. "One-shot device test data analysis using non-parametric and semi-parametric inferential methods and applications," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

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