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Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence

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  • Han, Xiao
  • Wang, Zili
  • Xie, Min
  • He, Yihai
  • Li, Yao
  • Wang, Wenzhuo

Abstract

The performance states of the manufacturing equipment and the quality states of the manufactured products are important indicators for the operational state evaluation and maintenance decision of the multi-state system. Further, the performance degradation of manufacturing components shows some dependence on the decline in product quality. However, the traditional remaining useful life (RUL) prediction and maintenance strategy of manufacturing system are limited to the dependence of the manufacturing components performance degradation. Based on the RUL prediction model that considers the components dependence for product quality requirements, a system predictive maintenance method based on the component functional importance is proposed. First, the connotation of degradation mechanism, functional dependence and RUL for manufacturing system is expounded. Second, a mission reliability oriented RUL prediction method for manufacturing systems is developed based on the functional dependence of components. Third, an approach for average maintenance cost calculation is proposed based on dynamic RUL prediction after each maintenance action, and the functional importance is applied to prioritize the predictive maintenance component-sets. Finally, the case results show that the proposed approach can ensure the ability of manufacturing systems to complete production tasks with high quality product, and reduce the maintenance cost in the production cycle simultaneously.

Suggested Citation

  • Han, Xiao & Wang, Zili & Xie, Min & He, Yihai & Li, Yao & Wang, Wenzhuo, 2021. "Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
  • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021001137
    DOI: 10.1016/j.ress.2021.107560
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    References listed on IDEAS

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