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Manufacturing system maintenance based on dynamic programming model with prognostics information

Author

Listed:
  • Qinming Liu

    (University of Shanghai for Science and Technology)

  • Ming Dong

    (Shanghai Jiao Tong University)

  • Wenyuan Lv

    (University of Shanghai for Science and Technology)

  • Chunming Ye

    (University of Shanghai for Science and Technology)

Abstract

The traditional maintenance strategies may result in maintenance shortage or overage, while deterioration and aging information of manufacturing system combined by single important equipment from prognostics models are often ignored. With the higher demand for operational efficiency and safety in industrial systems, predictive maintenance with prognostics information is developed. Predictive maintenance aims to balance corrective maintenance and preventive maintenance by observing and predicting the health status of the system. It becomes possible to integrate the deterioration and aging information into the predictive maintenance to improve the overall decisions. This paper presents an integrated decision model which considers both predictive maintenance and the resource constraint. First, based on hidden semi-Markov model, the system multi-failure states can be classified, and the transition probabilities among the multi-failure states can be generated. The upper triangular transition probability matrix is used to describe the system deterioration, and the changing of transition probability is used to denote the system aging process. Then, a dynamic programming maintenance model is proposed to obtain the optimal maintenance strategy, and the risks of maintenance actions are analyzed. Finally, a case study is used to demonstrate the implementation and potential applications of the proposed methods.

Suggested Citation

  • Qinming Liu & Ming Dong & Wenyuan Lv & Chunming Ye, 2019. "Manufacturing system maintenance based on dynamic programming model with prognostics information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1155-1173, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1314-6
    DOI: 10.1007/s10845-017-1314-6
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    References listed on IDEAS

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    Cited by:

    1. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.
    2. Michele Compare & Luca Bellani & Enrico Cobelli & Enrico Zio & Francesco Annunziata & Fausto Carlevaro & Marzia Sepe, 2020. "A reinforcement learning approach to optimal part flow management for gas turbine maintenance," Journal of Risk and Reliability, , vol. 234(1), pages 52-62, February.

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