Using ML-Based Models in Simulation of CPPSs: A Case Study of Smart Meter Production
摘要
Simulation models have proven successful in various CPPS tasks such as optimization, diagnosis or reconfiguration. However, creating these models is a costly process. This paper describes an approach which uses: 1) recorded data to automatically learn timed automata models of system components; and 2) manual logic based on prior knowledge that extends and enables the utilization of the learned models for simulation. Experiments in a smart meter production facility show: 1) a successful detection of a suboptimal configuration; 2) the identification of causes of a decrease in productivity; and 3) a correct assessment of possible actions after a disturbance has occurred.