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
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
