Talks and Poster Presentations (with Proceedings-Entry):

A. Mazak, M. Wimmer, P. Patsuk-Bösch:
"Reverse Engineering of Production Processes based on Markov Chains";
Talk: 13th IEEE Conference on Automation Science and Engineering (CASE 2017), Xi'an, China; 2017-08-20 - 2017-08-23; in: "Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE 2017)", IEEE, (2017), ISBN: 978-1-5090-6780-0; 680 - 686.

English abstract:
Understanding and providing knowledge of production
processes is crucial for flexible production systems as
many decisions are postponed to the operation time. Furthermore,
dealing with process improvements requires to have a
clear picture about the status of the currently employed process.
This becomes even more challenging with the emergence of
Cyber-Physical Production Systems (CPPS). However, CPPS
also provide the opportunity to observe the running processes
by using concepts from IoT to producing logs for reflecting the
events happening in the system during its execution.
Therefore, we propose in this paper a fully automated
approach for representing operational logs as models which
additionally allows analytical means. In particular, we provide
a transformation chain which allows the reverse engineering
of Markov chains from event logs. The reverse engineered
Markov chains allow to abstract the complexity of run-time
information as well as to enable what-if analysis whenever
improvements are needed by employing current model-based as
well as measurement-based technologies. We demonstrate the
approach based on a lab-sized transportation line system.

Created from the Publication Database of the Vienna University of Technology.