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An integrated model of statistical process control and maintenance based on the delayed monitoring

Author

Listed:
  • Yin, Hui
  • Zhang, Guojun
  • Zhu, Haiping
  • Deng, Yuhao
  • He, Fei

Abstract

This paper develops an integrated model of statistical process control and maintenance decision. The proposal of delayed monitoring policy postpones the sampling process till a scheduled time and contributes to ten-scenarios of the production process, where equipment failure may occur besides quality shift. The equipment failure and the control chart alert trigger the corrective maintenance and the predictive maintenance, respectively. The occurrence probability, the cycle time and the cycle cost of each scenario are obtained by integral calculation; therefore, a mathematical model is established to minimize the expected cost by using the genetic algorithm. A Monte Carlo simulation experiment is conducted and compared with the integral calculation in order to ensure the analysis of the ten-scenario model. Another ordinary integrated model without delayed monitoring is also established as comparison. The results of a numerical example indicate satisfactory economic performance of the proposed model. Finally, a sensitivity analysis is performed to investigate the effect of model parameters.

Suggested Citation

  • Yin, Hui & Zhang, Guojun & Zhu, Haiping & Deng, Yuhao & He, Fei, 2015. "An integrated model of statistical process control and maintenance based on the delayed monitoring," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 323-333.
  • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:323-333
    DOI: 10.1016/j.ress.2014.09.020
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    References listed on IDEAS

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    4. Boumallessa, Zeineb & Chouikhi, Houssam & Elleuch, Mounir & Bentaher, Hatem, 2023. "Modeling and optimizing the maintenance schedule using dynamic quality and machine condition monitors in an unreliable single production system," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Liu, Junqiang & Pan, Chunlu & Lei, Fan & Hu, Dongbin & Zuo, Hongfu, 2021. "Fault prediction of bearings based on LSTM and statistical process analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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    7. Liping Liu & Lining Jiang & Ding Zhang, 2017. "An integrated model of statistical process control and condition-based maintenance for deteriorating systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1452-1460, November.
    8. He, Yihai & Zhao, Yixiao & Han, Xiao & Zhou, Di & Wang, Wenzhuo, 2020. "Functional risk-oriented health prognosis approach for intelligent manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    9. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    10. Shuyuan Gan & Bolun Wang & Zhifang Song, 2021. "A Combined Maintenance Strategy Considering Spares, Buffer, and Quality," Journal of Risk and Reliability, , vol. 235(3), pages 431-445, June.
    11. Azimpoor, Samareh & Taghipour, Sharareh, 2021. "Joint inspection and product quality optimization for a system with delayed failure," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. Rasay, Hasan & Taghipour, Sharareh & Sharifi, Mani, 2022. "An integrated Maintenance and Statistical Process Control Model for a Deteriorating Production Process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
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