Abstract
Trying to meet the demands for flexibility while achieving high productivity often leads to highly complex production equipment, combining mechanical, electrical, and virtual functions into cyber-physical systems (CPS). This increasing complexity can only be faced with the advanced autonomy of production systems, supporting in running, analyzing, and optimizing the equipment. A critical enabler to achieving this is modeling the system's state and executed process. However, this modeling requires extensive manual effort and expert knowledge. Automated process discovery approaches from the domain of process mining (PM) seem promising in reducing this effort, however, utilizing the low-level machine controller events results in models too complex for human interpretation. Therefore, we introduce an approach capable of clustering low-level events from I/O signals of programmable logic controllers (PLCs) to higher-level system states to automatically discover human-understandable process models of manufacturing systems, supporting analysis and optimization
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