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Optimal maintenance policy incorporating system level and unit level for mechanical systems

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  • Chaoqun Duan
  • Chao Deng
  • Bingran Wang

Abstract

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.

Suggested Citation

  • Chaoqun Duan & Chao Deng & Bingran Wang, 2018. "Optimal maintenance policy incorporating system level and unit level for mechanical systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(5), pages 1074-1087, April.
  • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:5:p:1074-1087
    DOI: 10.1080/00207721.2018.1432782
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    Cited by:

    1. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.

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