基于数字孪生的车间扰动判别与重调度研究

Research on workshop disturbance identification and rescheduling based on digital twin

针对车间生产过程中扰动事件频发、扰动影响程度难以判别以及现有重调度方案应用不理想等问题,提出一种基于数字孪生的车间重调度方法,建立了扰动分层判别机制,分别采用数字孪生虚拟模型与支持向量机(Support Vector Machine,SVM)分类决策模型识别显性与隐性扰动,为重调度触发提供决策;通过算法与数字孪生虚拟模型协同优化,得到兼具效率和稳定性的重调度方案。以某大型机械制造工厂为例,仿真实验结果表明,建立的扰动分层判别机制能对车间扰动类型进行有效判别,通过数字孪生虚拟模型仿真输出性能指标能够判定显性扰动,SVM分类决策模型对于隐性扰动的识别具有较高的准确率;基于数字孪生的车间重调度方案可得到效率较高、工序变动成本较低的重调度方案,提高了车间的生产性能。

In view of the frequent disturbance events in workshop production,the difficulty in distinguishing the disturbance impact and the unsatis factory application of existing rescheduling schemes,a workshop rescheduling solution was proposed based on the digital twin technology.A disturbance hierarchy identification mechanism was established,in which the digital twin virtual model and Support Vector Machine(SVM) classification decision model were applied respectively to identify explicit and recessive disturbances.Then decisions were provided fortriggering rescheduling.The co-optimization approach of simulation model and algorithm was proposed to obtain a rescheduling plan with both efficiency and stability.Taken a large machinery manufacturing factory as an example,the simulation results show that the workshop disturbances are identified effectively by the established hierarchy identification mechanism.The explicit disturbanceis can be determined by the simulation output performance index of digital twin virtual model,while the SVM classification decision model has high identification accuracy of recessive disturbances.A rescheduling plan with high efficiency and less process change cost is obtained based on digital twin rescheduling,which could improve the production performance of the workshop.