Learning Behaviour Models of Discrete Event Production Systems from Observing Input/Output Signals

Abstract Learning behavior models out of event traces has been tackled in a wide variety of scientific projects and publications. Usually the resulting models are used for fault detection, reengineering, and analysis. But in practical applications, like monitoring, learned models can show high complexity and permissivity which makes it difficult to use these models and results tend to be ambiguous. Therefore, this paper defines so called Machine State Petri Nets (MSPN) with the aim of being generated out of recorded event traces and exploit additional information of the system to reduce the permissivity. An already existing learning algorithm has been extended by exploiting some facts common for most practical applications. An example shows how these adaptations improve the base algorithm regarding the aforementioned requirements.