计算机集成制造系统 ›› 2019, Vol. 25 ›› Issue (第9): 2149-2158.DOI: 10.13196/j.cims.2019.09.003

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面向健康保障的制造系统预测性维修决策模型

谷长超1,2,何益海1+,韩笑1,陈兆祥1   

  1. 1.北京航空航天大学可靠性与系统工程学院
    2.中国运载火箭技术研究院
  • 出版日期:2019-09-30 发布日期:2019-09-30
  • 基金资助:
    国家自然科学基金资助项目(61473017);‘十三五’装备预研领域基金资助项目(6140002050116HK01001)。

Decision-making model of predictive maintenance for manufacturing systems health protection

  • Online:2019-09-30 Published:2019-09-30
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.61473017),and the‘Thirteenth Five-Year Plan’ Equipment Pre-research Field Fund,China(No.6140002050116HK01001).

摘要: 为实现制造系统稳定完成生产任务并输出高质量产品的目标,提出一种面向健康保障的制造系统预测性维修决策方法。针对制造系统的动态构成,从任务执行状态、设备性能状态以及产品质量状态3方面综合描述了其运行过程;建立了一种综合考虑设备性能多态性与任务要求多变性的时序动态任务可靠性预测模型,并提出了质量偏差指标量化方法以实现产品质量的矢量表达;以任务可靠性为决策变量,引入输出产品质量控制,以任务周期生产总费用最小为目标进行了维修策略优化。以某发动机缸盖制造系统为例开展了方法验证,结果表明,应用该决策方法比传统预测性维修与等周期预防性维修分别节约了18.41%和23.67%的生产费用。

关键词: 预测性维修, 制造系统, 健康状态, 任务可靠性, 产品质量

Abstract: To ensure that the manufacturing system can accomplish the production tasks stably and output high quality products,a decision-making method of predictive maintenance for health protection was proposed.The operation process of complex manufacturing systems was comprehensively described from three aspects:task execution states,equipment performance states and product quality states.By considering the multi-state of equipment performance and variability of task requirements,a prognosis model of dynamic mission reliability was established,and the quantification index of quality deviation to realize the vector expression of product quality was put forward.Taking the mission reliability as the decision variable and integrating the output product quality control,the maintenance strategy was optimized with the goal of minimizing the total production cost in the mission cycle.A method validation was performed with an engine cylinder head manufacturing system as the example,and final results showed that the proposed method achieved approximately 23.67% and 18.41% cost saving over periodic preventive maintenance and conventional predictive maintenance respectively.

Key words: predictive maintenance, manufacturing system, health status, mission reliability, product quality

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