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Petri nets are a common modeling approach for parallel processes such as transport operations in conveying systems. In industrial applications, the Petri net models are usually created manually, which involves a lot of effort, especially if the modeled systems change frequently. This paper introduces a new learning method to automatically generate Petri nets from sensor data acquired in conveying systems. The underlying approach is to create prefix and postfix trees of possible event sequences and to merge them into a compact graph, which can be transformed into a deterministic Petri net model of the conveying system. Experimental results show that the proposed method produces realistic Petri net models even for conveying systems with ambiguous events.

Learning Petri net models from sensor data of conveying systems based on the merging of prefix and postfix trees