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Prognostic control-enhanced maintenance optimization for multi-component systems

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  • Niu, Gang
  • Jiang, Junjie

Abstract

In the last decades, the fast evolution of the industrial scenario has boosted the economic relevance of maintenance in all sectors of industry, and interests in maintenance can be expected to continue increasing in the next future. Maintenance has gained in importance as a support function for ensuring equipment availability, quality products, on-time deliveries, and plant safety. This paper presents a novel maintenance optimization strategy for multi-component systems that uses local prognostic control to improve health statue of operating systems at component-level and solves a global optimal problem to find proper maintenance interval at system-level, the combination of which can increase maintenance economy effectively. The proposed strategy can be demonstrated by a case study of braking system of rail vehicles. Compared with traditional dynamic maintenance methodologies, the results show that the maintenance cost can be reduced significantly when both health-oriented prognostic control and global optimization are utilized together.

Suggested Citation

  • Niu, Gang & Jiang, Junjie, 2017. "Prognostic control-enhanced maintenance optimization for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 218-226.
    • Handle: RePEc:eee:reensy:v:168:y:2017:i:c:p:218-226
    DOI: 10.1016/j.ress.2017.04.011
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    References listed on IDEAS

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    Cited by:

    1. Wu, Shaomin & Do, Phuc, 2017. "Editorial," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 1-3.
    2. Yousra El kihel & Ali El kihel & El Mahdi Bouyahrouzi, 2022. "Contribution of Maintenance 4.0 in Sustainable Development with an Industrial Case Study," Sustainability, MDPI, vol. 14(17), pages 1-26, September.
    3. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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