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A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds

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  • Poppe, Joeri
  • Boute, Robert N.
  • Lambrecht, Marc R.

Abstract

Condition-based maintenance (CBM) makes use of the actual condition of the component to decide when to maintain and/or replace the component, thereby maximising the lifetime of the machine, while minimising the number of service interventions. In this paper we combine CBM on one (monitored) component, with periodic preventive maintenance (PM) and corrective maintenance (CM) on the other components of the same machine/system. We implement two thresholds on the degradation level to decide when to service the monitored component: when the degradation level of the monitored component surpasses a first ‘opportunistic’ threshold, the monitored component will be serviced together with other components, for instance with a (planned) PM intervention, or upon breakdown of another component, requiring CM. In case none of these opportunities have taken place, and the degradation level surpasses a second ‘intervention’ threshold, an additional maintenance intervention is planned for the monitored component in order to prevent a failure. Both thresholds are optimised to minimise the total expected maintenance costs of the monitored component, or to minimise the downtime of the machine due to maintenance on the monitored component. We perform an extensive numerical experiment to demonstrate the potential gains of this hybrid policy with two thresholds compared to using a traditional PM policy, and we identify its key drivers of performance. We also benchmark our results when only one threshold is implemented. Our model is validated and applied at an OEM in the compressed air and generator industry.

Suggested Citation

  • Poppe, Joeri & Boute, Robert N. & Lambrecht, Marc R., 2018. "A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds," European Journal of Operational Research, Elsevier, vol. 268(2), pages 515-532.
    • Handle: RePEc:eee:ejores:v:268:y:2018:i:2:p:515-532
    DOI: 10.1016/j.ejor.2018.01.039
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    Cited by:

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    3. Kivanç, İpek & Fecarotti, Claudia & Raassens, Néomie & van Houtum, Geert-Jan, 2024. "A scalable multi-objective maintenance optimization model for systems with multiple heterogeneous components and a finite lifespan," European Journal of Operational Research, Elsevier, vol. 315(2), pages 567-579.
    4. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    5. Vincent F. Yu & Thi Huynh Anh Le & Tai-Sheng Su & Shih-Wei Lin, 2021. "Optimal Maintenance Policy for Offshore Wind Systems," Energies, MDPI, vol. 14(19), pages 1-19, September.
    6. Castro, Inma T. & Basten, Rob J.I. & van Houtum, Geert-Jan, 2020. "Maintenance cost evaluation for heterogeneous complex systems under continuous monitoring," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    7. van Staden, Heletjé E. & Deprez, Laurens & Boute, Robert N., 2022. "A dynamic “predict, then optimize” preventive maintenance approach using operational intervention data," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1079-1096.
    8. Wang, Jinhe & Zhang, Xiaohong & Zeng, Jianchao & Zhang, Yunzheng, 2020. "Joint external and internal opportunistic optimisation for wind turbine considering wind velocity," Renewable Energy, Elsevier, vol. 159(C), pages 380-398.
    9. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    10. Nooshin Salari & Viliam Makis, 2020. "Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(7), pages 548-558, October.
    11. van Staden, Heletjé E. & Boute, Robert N., 2021. "The effect of multi-sensor data on condition-based maintenance policies," European Journal of Operational Research, Elsevier, vol. 290(2), pages 585-600.
    12. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    13. Joseph M. Southgate & Katrina Groth & Peter Sandborn & Shapour Azarm, 2024. "Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems," Papers 2405.09519, arXiv.org.
    14. Zhang, Xiaohong & Liao, Haitao & Zeng, Jianchao & Shi, Guannan & Zhao, Bing, 2021. "Optimal Condition-based Opportunistic Maintenance and Spare Parts Provisioning for a Two-unit System using a State Space Partitioning Approach," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

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