IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v228y2014i1p52-61.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X13495777
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1748006X13495777?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
    2. Moshe Shaked & J. George Shanthikumar, 1986. "Multivariate Imperfect Repair," Operations Research, INFORMS, vol. 34(3), pages 437-448, June.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tanwar, Monika & Rai, Rajiv N. & Bolia, Nomesh, 2014. "Imperfect repair modeling using Kijima type generalized renewal process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 24-31.
    2. Francesco Corman & Sander Kraijema & Milinko Godjevac & Gabriel Lodewijks, 2017. "Optimizing preventive maintenance policy: A data-driven application for a light rail braking system," Journal of Risk and Reliability, , vol. 231(5), pages 534-545, October.
    3. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    4. Pham, Hoang & Wang, Hongzhou, 1996. "Imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 94(3), pages 425-438, November.
    5. Richard Arnold & Stefanka Chukova & Yu Hayakawa, 2016. "Failure distributions in multicomponent systems with imperfect repairs," Journal of Risk and Reliability, , vol. 230(1), pages 4-17, February.
    6. Xiang, Yisha, 2013. "Joint optimization of X¯ control chart and preventive maintenance policies: A discrete-time Markov chain approach," European Journal of Operational Research, Elsevier, vol. 229(2), pages 382-390.
    7. Zhao, Xiujie & He, Shuguang & Xie, Min, 2018. "Utilizing experimental degradation data for warranty cost optimization under imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 108-119.
    8. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    9. Seyed Habib A. Rahmati & Abbas Ahmadi & Kannan Govindan, 2018. "A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach," Annals of Operations Research, Springer, vol. 269(1), pages 583-621, October.
    10. Guo R. & Ascher H. & Love E., 2001. "Towards Practical and Synthetical Modelling of Repairable Systems," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 147-182, January.
    11. Raouf, BOUCEKKINE & Blanca, MARTINEZ & Cagri, SAGLAM, 2006. "Capital Maintenance Vs Technology Adopton under Embodied Technical Progress," Discussion Papers (ECON - Département des Sciences Economiques) 2006030, Université catholique de Louvain, Département des Sciences Economiques.
    12. Dewan, Isha & Dijoux, Yann, 2015. "Modelling repairable systems with an early life under competing risks and asymmetric virtual age," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 215-224.
    13. Chen, Yiming & Liu, Yu & Jiang, Tao, 2021. "Optimal maintenance strategy for multi-state systems with single maintenance capacity and arbitrarily distributed maintenance time," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    14. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    15. Ruey Yeh & Wen Chang & Hui-Chiung Lo, 2010. "Optimal threshold values of age and two-phase maintenance policy for leased equipments using age reduction method," Annals of Operations Research, Springer, vol. 181(1), pages 171-183, December.
    16. Shen, Jingyuan & Cui, Lirong & Ma, Yizhong, 2019. "Availability and optimal maintenance policy for systems degrading in dynamic environments," European Journal of Operational Research, Elsevier, vol. 276(1), pages 133-143.
    17. Xiao, Lei & Zhang, Xinghui & Tang, Junxuan & Zhou, Yaqin, 2020. "Joint optimization of opportunistic maintenance and production scheduling considering batch production mode and varying operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    18. Jackson, Canek & Pascual, Rodrigo, 2008. "Optimal maintenance service contract negotiation with aging equipment," European Journal of Operational Research, Elsevier, vol. 189(2), pages 387-398, September.
    19. Navarro, Jorge & Arriaza, Antonio & Suárez-Llorens, Alfonso, 2019. "Minimal repair of failed components in coherent systems," European Journal of Operational Research, Elsevier, vol. 279(3), pages 951-964.
    20. Lu, Biao & Zhou, Xiaojun, 2017. "Opportunistic preventive maintenance scheduling for serial-parallel multistage manufacturing systems with multiple streams of deterioration," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 116-127.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:228:y:2014:i:1:p:52-61. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.