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Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network

Author

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  • Yihai He
  • Changchao Gu
  • Xiao Han
  • Jiaming Cui
  • Zhaoxiang Chen

Abstract

Multi-state-oriented mission reliability modeling is the premise of intelligent scheduling and predictive maintenance for the multi-station manufacturing system. Previous studies on reliability modeling for manufacturing system could only provide a static reliability model based on the basic reliability of the components of manufacturing systems, which cannot support reliability-oriented production scheduling and preventive maintenance effectively. To resolve this dilemma, a multi-state-oriented mission reliability modeling for multi-station manufacturing system is proposed. First, the mapping relationship between the produced product reliability and mission reliability of the manufacturing system is proposed as the basis for modeling, and the connotation of mission reliability is elaborated by analyzing the polymorphisms of the multi-station manufacturing system. Second, a graphical representation to improve the state transparency named as Quality State Task Network is proposed based on production data by integrating the variability of task-demands propagation as well as the multi-state in material quality and machine performance. Third, the mission reliability modeling method based on the Quality State Task Network is proposed. Finally, a case study of cylinder-head manufacturing system has been applied to validate the proposed model.

Suggested Citation

  • Yihai He & Changchao Gu & Xiao Han & Jiaming Cui & Zhaoxiang Chen, 2017. "Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network," Journal of Risk and Reliability, , vol. 231(6), pages 701-715, December.
    • Handle: RePEc:sae:risrel:v:231:y:2017:i:6:p:701-715
    DOI: 10.1177/1748006X17728599
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    References listed on IDEAS

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    1. Agha, Mujtaba H. & Thery, Raphaele & Hetreux, Gilles & Hait, Alain & Le Lann, Jean Marc, 2010. "Integrated production and utility system approach for optimizing industrial unit operations," Energy, Elsevier, vol. 35(2), pages 611-627.
    2. Jingshan Li & Emre Enginarlar & Semyon Meerkov, 2004. "Random Demand Satisfaction in Unreliable Production–Inventory–Customer Systems," Annals of Operations Research, Springer, vol. 126(1), pages 159-175, February.
    3. Lu, Ji-Min & Wu, Xiao-Yue, 2014. "Reliability evaluation of generalized phased-mission systems with repairable components," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 136-145.
    4. Farouq Alhourani, 2016. "Cellular manufacturing system design considering machines reliability and parts alternative process routings," International Journal of Production Research, Taylor & Francis Journals, vol. 54(3), pages 846-863, February.
    5. Lin, Yi-Kuei & Chang, Ping-Chen, 2012. "Evaluate the system reliability for a manufacturing network with reworking actions," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 127-137.
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