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Deployment of Prognostics to Optimize Aircraft Maintenance – A Literature Review

Author

Listed:
  • J.P. Sprong

    (Delft University of Technology, Delft, Zuid-Holland, The Netherlands)

  • X. Jiang

    (Delft University of Technology, Delft, Zuid-Holland, The Netherlands)

  • H. Polinder

    (Delft University of Technology, Delft, Zuid-Holland, The Netherlands)

Abstract

Historic records show that the cost of operating and supporting an aircraft may exceed the initial purchase price as much as ten times. Maintenance, repair and overhaul activities rep- resent around 10-15% of an airlines annual operational costs. Therefore, optimization of maintenance operations to minimize cost is extremely important for airlines in order to stay competitive. Prognostics, a process to predict remaining useful life of systems and/ or components suffering from aging or degradation, has been recognized as one of the revolutionary disciplines that can improve efficiency of aircraft operations and optimize aircraft maintenance. This study focuses on literature that has used prognostics to optimize aircraft maintenance and identifies research gaps for further optimization of aircraft maintenance in commercial aviation. In this paper, the origin and development of prognostics is firstly introduced. Thereafter, the state of art of aircraft maintenance is reviewed. Next, the applicability of prognostics to optimize aircraft maintenance is explained, reviewed, and potential challenges and opportunities are explored. Finally, the state-of-the-art of prognostics in aircraft maintenance is dis- cussed and research gaps are identified in perspective of the deployment of prognostics to optimize aircraft maintenance.

Suggested Citation

  • J.P. Sprong & X. Jiang & H. Polinder, 2020. "Deployment of Prognostics to Optimize Aircraft Maintenance – A Literature Review," Journal of International Business Research and Marketing, Inovatus Services Ltd., vol. 5(4), pages 26-37, May.
    • Handle: RePEc:mgs:jibrme:v:5:y:2020:i:4:p:26-37
    DOI: 10.18775/jibrm.1849-8558.2015.54.3004
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    References listed on IDEAS

    as
    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Feng, Qiang & Bi, Xiong & Zhao, Xiujie & Chen, Yiran & Sun, Bo, 2017. "Heuristic hybrid game approach for fleet condition-based maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 166-176.
    3. Lin Lin & Bin Luo & ShiSheng Zhong, 2018. "Multi-objective decision-making model based on CBM for an aircraft fleet with reliability constraint," International Journal of Production Research, Taylor & Francis Journals, vol. 56(14), pages 4831-4848, July.
    4. Zhao, Zeqi & Bin Liang, & Wang, Xueqian & Lu, Weining, 2017. "Remaining useful life prediction of aircraft engine based on degradation pattern learning," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 74-83.
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    Cited by:

    1. Tseremoglou, Iordanis & Santos, Bruno F., 2024. "Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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    More about this item

    Keywords

    Prognostics; Optimization; Deployment; Aircraft maintenance; Repair-induced failures; Predictive Maintenance;
    All these keywords.

    JEL classification:

    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

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