We provide on this page the different artefacts which have been used and created when performing the case study concerning the IAF plant.


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.

Case Study Description

For the purpose of validating our reverse engineering approach of production processes, we developed a prototypical implementation of the introduced framework in the context of the IAF plant. For this purpose, we used the lab-size production system hosted at IAF of the Otto-v.-Guericke University Magedburg as reference example.

We implemented a SysML-based simulator for the IAF plant case study to be able to produce log files by using our OperationsAndTraceMonitor tool. The produced log files are the input for the UserTrace2Markov tool, which produces a transition probability matrix of the underlying Markov chain for the Area 2 of the IAF plant.

In order to visualize the results of this matrix, as well as, the statistical performance information in a graphical modeling language, we additionally developed a specific modeling editor. This editor supports a domain-specific modeling language (DSML) for representing the resulting information to engineers.

All menioned artefacts of the case study are provided as open source.


Running the Case Study

For running the case study, we recommend to use the Eclipse Modeling Edition. In the following we provide information on how to download, run and setup the Eclipse bundle for elaborating the case study.

Download Eclipse

First you have to download the Eclipse Modeling Tools (version: Neon R) for your operating system and architecture. Once the download is finished, unzip the downloaded archive to any location you prefer. Please note, however, that the location should be writable without additional user permissions so that Eclipse may autonomously install updates and additional plug-ins.

Run Eclipse

The prerequisite for running Eclipse is a current version of the Java Runtime Environment. Eclipse itself does not have to be “installed” per se. You may directly start Eclipse by running eclipse (.exe in case you use Windows). Select a workspace location according to your personal preferences.