Manufacturing system maintenance based on dynamic programming model with prognostics information
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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.
KeywordsMaintenance Dynamic programming Prognosis Deterioration Aging
The work presented in this paper has been supported by Grants from National Natural Science Foundation of China (Nos. 71471116, 71131005 and 71271138), “Pu Jiang” Project (No. 14PJC077) of Science and Technology Commission of Shanghai Municipality, Humanity and Social Science Youth foundation of Ministry of Education of China (No. 15YJCZH096), Hujiang Foundation–Humanity and Social Science “Climbing” Program of University of Shanghai for Science and Technology (No. 16HJPD-B04), Programs of National Training Foundation of University of Shanghai for Science and Technology (No. 16HJPYQN02), Doctoral Startup Foundation Project of University of Shanghai for Science and Technology (No. BSQD201403). Authors are indebted to the reviewers and the editors for their constructive comments which greatly improved the contents and exposition of this paper.
- Li, R., & Ryan, J. K. (2011). A Bayesian inventory model using real-time condition monitoring information. Production and Operations Management, 20(5), 754–771.Google Scholar