4.7 Article

Mission Reliability Evaluation for Fuzzy Multistate Manufacturing System Based on an Extended Stochastic Flow Network

Journal

IEEE TRANSACTIONS ON RELIABILITY
Volume 69, Issue 4, Pages 1239-1253

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2019.2957502

Keywords

Manufacturing systems; Task analysis; Reliability theory; Stochastic processes; Modeling; Extend stochastic flow network (ESFN); fuzzy Markov model; fuzzy multistate manufacturing system; mission reliability; reliability evaluation

Funding

  1. National Natural Science Foundation of China [71971181]
  2. National Defense Pre-Research Foundation of China by General Project [JZX7Y20190242012401]

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Due to the artificial division of a machine performance state and unpredictable external working conditions, the performance and state transition strength of multistate machines (that is, their fuzzy multistate characteristic) cannot be accurately identified. Moreover, existing research on the reliability evaluation of multistate systems does not consider the operating mechanism of a manufacturing system. Therefore, this article proposes a mission reliability evaluation method for fuzzy multistate manufacturing systems based on an extended stochastic flow network (ESFN). First, from the relationship between the production task execution state, production machine degradation state, and produced product quality state, the operation mechanism of a fuzzy multistate manufacturing system is proposed, and the connotation of mission reliability is further defined. Second, an ESFN model of the multistate manufacturing system based on the operating mechanism of a fuzzy multistate manufacturing system is established. Third, on the basis of a fuzzy Markov model, the mission reliability evaluation method of the fuzzy multistate manufacturing system is proposed. Finally, a case study of the manufacturing system for producing a ferrite phase shifting unit is presented to verify the proposed approach, and a sensitivity analysis is conducted to analyze the impacts of the parameters in the proposed model.

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