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A review of condition-based maintenance decision-making

Author

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  • Rosmaini Ahmad
  • Shahrul Kamaruddin

Abstract

Condition-based maintenance (CBM) has been a research topic since 1975. It has been introduced as an alternative approach to enhance the effectiveness of preventive maintenance strategy. Currently, CBM research is growing rapidly. Compared with the traditional time-based maintenance approach, CBM application is more beneficial and realistic. With CBM, better maintenance decisions can be made to avoid or minimise unnecessary maintenance costs. This paper attempts to explore how exactly CBM decision-making is conducted, with the methods of decision-making classified into current-condition-evaluation-based and future-condition-prediction-based. This paper systematically reviews the applications of these methods by focusing on the techniques used, as well as on case studies. It concludes with findings based on the academic and industrial perspectives. [Received 10 July 2010; Revised 9 November 2010; Accepted 24 February 2011]

Suggested Citation

  • Rosmaini Ahmad & Shahrul Kamaruddin, 2012. "A review of condition-based maintenance decision-making," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(5), pages 519-541.
    • Handle: RePEc:ids:eujine:v:6:y:2012:i:5:p:519-541
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    Citations

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    Cited by:

    1. Huynh, K.T., 2021. "An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Nguyen, Kim-Anh & Do, Phuc & Grall, Antoine, 2017. "Joint predictive maintenance and inventory strategy for multi-component systems using Birnbaum’s structural importance," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 249-261.
    3. Ramin Moghaddass & Şeyda Ertekin, 2018. "Joint optimization of ordering and maintenance with condition monitoring data," Annals of Operations Research, Springer, vol. 263(1), pages 271-310, April.
    4. Huynh, K.T. & Grall, A. & Bérenguer, C., 2017. "Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 237-254.
    5. E. Skordilis & R. Moghaddass, 2017. "A condition monitoring approach for real-time monitoring of degrading systems using Kalman filter and logistic regression," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5579-5596, October.

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