Black-box identification of discrete event systems with optimal partitioning of concurrent subsystems

This paper proposes a data-driven method to determine concurrent parts in Discrete Event Systems (DES). The aim is to improve the results of black-box identification methods without considering any system information except of observed data. To allow an analysis of the collected data, the impact of concurrency on the exhibited system data is determined by two criteria. We propose to use an optimization algorithm that isolates concurrent parts of the system by minimizing concurrency expressed by the two proposed criteria within the determined subsystems. A lab-size application shows the potential of the method for real-world manufacturing systems. The aim is to deliver optimal identified models for fault detection and isolation.