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Availability-based optimal maintenance policies for repairable systems in military aviation by identification of dominant failure modes

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  • Rajiv N Rai
  • Nomesh Bolia

Abstract

Modeling of imperfect repair through perfect renewal process uses an “as good as new†repair assumption and nonhomogeneous Poisson process uses an “ABAO†repair assumption. In practice, repair actions do not result in such extreme situations but in a complex transitional one, that is, general repair. This article discusses generalized renewal process for an aero engine as repairable component. Maximum likelihood estimators for the reliability parameters are estimated using generalized renewal process, for field failure data of an aero engine. The current practice designates repairable components, as high failure rate components based on intuition, experience and the number of unscheduled failures at repair depots. A methodology is developed to designate high failure rate components based on availability by taking into consideration the dominant failure modes of the aero engine. Then, a comparison is made with a “Black Box†approach. The present maintenance policy is then reviewed by reducing the present time between overhauls for the high failure rate components. We observe a noteworthy enhancement in all the performance parameters in the suggested maintenance policy. We also observe that percent improvement achieved in all performance parameters on reducing the next overhaul cycle time, with failure modes analysis is more than when failure modes are not considered.

Suggested Citation

  • Rajiv N Rai & Nomesh Bolia, 2014. "Availability-based optimal maintenance policies for repairable systems in military aviation by identification of dominant failure modes," Journal of Risk and Reliability, , vol. 228(1), pages 52-61, February.
  • Handle: RePEc:sae:risrel:v:228:y:2014:i:1:p:52-61
    DOI: 10.1177/1748006X13495777
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    References listed on IDEAS

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    1. Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
    2. Kijima, Masaaki & Nakagawa, Toshio, 1992. "Replacement policies of a shock model with imperfect preventive maintenance," European Journal of Operational Research, Elsevier, vol. 57(1), pages 100-110, February.
    3. Krivtsov, V. & Yevkin, O., 2013. "Estimation of G-renewal process parameters as an ill-posed inverse problem," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 10-18.
    4. Moshe Shaked & J. George Shanthikumar, 1986. "Multivariate Imperfect Repair," Operations Research, INFORMS, vol. 34(3), pages 437-448, June.
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