Journal of Intelligent Manufacturing

, Volume 30, Issue 3, pp 1155–1173 | Cite as

Manufacturing system maintenance based on dynamic programming model with prognostics information

  • Qinming LiuEmail author
  • Ming Dong
  • Wenyuan Lv
  • Chunming Ye


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.


Maintenance 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.


  1. Annibale, P., Rocco, O., Penta, Di, Massimiliano, D. P., & Andrea, D. L. (2015). Improving multi-objective test case selection by injecting diversity in genetic algorithms. IEEE Transactions on Software Engineering, 41(4), 358–383.CrossRefGoogle Scholar
  2. Bartholomew-Biggs, M., Zuo, M. J., & Li, X. H. (2009). Modeling and optimizing sequential imperfect preventive maintenance. Reliability Engineering and System Safety, 94(1), 53–62.CrossRefGoogle Scholar
  3. Basten, R. J. I., van der Heijden, M. C., & Schutten, J. M. J. (2012). Joint optimization of level of repair analysis and spare parts stocks. European Journal of Operational Research, 222(3), 474–483.CrossRefGoogle Scholar
  4. Christer, A. H. (1999). Developments in delay time analysis for modeling plant maintenance. Journal of the Operational Research Society, 50, 1120–1137.CrossRefGoogle Scholar
  5. Fitouhi, M. C., & Nourelfath, M. (2012). Integrating noncyclical preventive maintenance scheduling and production planning for a single machine. International Journal of Production Economics, 136(1), 344–351.CrossRefGoogle Scholar
  6. Fitouhi, M. C., & Nourelfath, M. (2014). Integrating noncyclical preventive maintenance scheduling and production planning for multi-state systems. Reliability Engineering and System Safety, 121, 175–186.CrossRefGoogle Scholar
  7. Huynh, K. T., Castro, I. T., Barros, A., & Bérenguer, C. (2012). Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks. European Journal of Operational Research, 218(1), 140–151.CrossRefGoogle Scholar
  8. Jafari, L., & Makis, V. (2015). Joint optimal lot sizing and preventive maintenance policy for a production facility subject to condition monitoring. International Journal of Production Economics, 169, 156–168.CrossRefGoogle Scholar
  9. Jafari, L., & Makis, V. (2016). Optimal lot-sizing and maintenance policy for a partially observable production system. Computers and Industrial Engineering, 93, 88–98.CrossRefGoogle Scholar
  10. Kenne, J. P., & Nkeungoue, L. J. (2008). Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system. Applied Numerical Mathematics, 58(2), 180–194.CrossRefGoogle Scholar
  11. 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
  12. Liu, Q. M., Dong, M., Lv, W. Y., Geng, X. L., & Li, Y. P. (2015). A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosi. Mechanical Systems and Signal Processing, 64–65, 217–232.CrossRefGoogle Scholar
  13. Liu, Q. M., Dong, M., & Peng, Y. (2013). A dynamic predictive maintenance model considering spare parts inventory based on hidden semi-Markov model. Journal of Mechanical Engineering Science, 227(9), 2090–2103.CrossRefGoogle Scholar
  14. Lu, Z. Q., Cui, W. W., & Han, X. L. (2015). Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty. Computers and Industrial Engineering, 80, 236–244.CrossRefGoogle Scholar
  15. Marseguerra, M., Zio, E., & Podofillini, L. (2002). Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation. Reliability Engineering and System Safety, 77(2), 151–166.CrossRefGoogle Scholar
  16. Molavi, H., & Zahiri, A. (2015). Condition monitoring and fault diagnosis of an MR load tap changer using oil analysis data. Journal of Engineering Research, 3(2), 41–58.CrossRefGoogle Scholar
  17. Park, C. W., & Lee, H. S. (2011). A multi-class closed queuing maintenance network model with a parts inventory system. Computers & Operations Research, 38(11), 1584–1595.CrossRefGoogle Scholar
  18. Rausch, M., & Liao, H. T. (2010). Joint production and spare part inventory control strategy driven by condition based maintenance. IEEE Transaction on Reliability, 59(3), 507–516.CrossRefGoogle Scholar
  19. Van Horenbeek, A., Bure, J., Cattrysse, D., Pintelon, L., & Vansteenwegen, P. (2013). Joint maintenance and inventory optimization systems: A review. International Journal of Production Economics, 143(2), 499–508.CrossRefGoogle Scholar
  20. Wang, W. B. (2011). A joint spare part and maintenance inspection optimisation model using the delay-time concept. Reliability Engineering and System Safety, 96(11), 1535–1541.CrossRefGoogle Scholar
  21. Wang, W. B. (2012). A stochastic model for joint spare parts inventory and planned maintenance optimization. European Journal of Operational Research, 216(1), 127–139.CrossRefGoogle Scholar
  22. Wang, L., Chu, J., & Mao, W. J. (2008). A condition-based order-replacement policy for a single-unit system. Applied Mathematical Modelling, 32, 2274–2289.CrossRefGoogle Scholar
  23. Wang, W., Scarf, P. A., & Smith, M. A. J. (2000). On the application of a model of condition-based maintenance. Journal of the Operational Research Society, 51, 1218–1227.CrossRefGoogle Scholar
  24. Wang, X., Wang, H. W., & Qi, C. (2016). Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system. Journal of Intelligent Manufacturing, 27(2), 325–333.CrossRefGoogle Scholar
  25. Wang, Y. H., Deng, C., Wu, J., & Xiong, Y. (2015). Failure time prediction for mechanical device based on the degradation sequence. Journal of Intelligent Manufacturing, 26(6), 1181–1199.CrossRefGoogle Scholar
  26. Wu, F., Wang, T., & Lee, J. (2010). An online adaptive condition-based maintenance method for mechanical systems. Mechanical Systems and Signal Processing, 24(8), 2985–2995.CrossRefGoogle Scholar
  27. Zhong, C., & Jin, H. (2014). A novel optimal preventive maintenance policy for a cold standby system based on semi-Markov theory. European Journal of Operational Research, 232(2), 405–411.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Qinming Liu
    • 2
    Email author
  • Ming Dong
    • 1
  • Wenyuan Lv
    • 2
  • Chunming Ye
    • 2
  1. 1.Department of Operations Management, Antai College of Economics and ManagementShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Industrial Engineering, Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina

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