Towards Automatic Generation of Formal Models for Highly Automated Manufacturing Systems
The need for having digitalized tools that support operators and engineers was identified in a survey that was conducted to understand the problems faced during maintenance of manufacturing systems. To this end, this thesis provides an architecture that can be applied on old legacy systems as well as new state-of-the-art systems to collect data from the factory floor. The data obtained can be visualized in the form of Gantt charts to help operators keep track of the execution of the station. Furthermore, a model that captures the behavior of the system can be created by applying Process Mining algorithms to the collected data.
Model-based techniques have shown to be beneficial in developing control logic for highly automated and flexible manufacturing systems, as these techniques offer tools to test and formally verify the control logic to guarantee its correctness. These formal tools operate on such a model of the behavior of the system. However, manually constructing a model on which these tools can be applied is a tedious and error prone task, seldom deemed to be worth the effort. Thus, supporting engineers to build models will improve the adoption of formal tools within the manufacturing industry.
In order to obtain a formal model during the early development phase of the manufacturing system, this thesis studies the possibility to automatically infer a model of a system by interacting with its digital twin. The suggested L+ algorithm, an extension of the well-known L* algorithm, shows that it is possible to automatically build formal models in this way. Additionally, certain shortcomings are identified and need to be addressed before being able to these methods in a practical setting.
Ashfaq Hussain Farooqui
Chalmers, Elektroteknik, System- och reglerteknik, Automation
Error handling within highly automated automotive industry: Current practice and research needs
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA,; Vol. 2016-November(2016)p. Art no 7733628-
Paper i proceeding
From factory floor to process models: A data gathering approach to generate, transform, and visualize manufacturing processes
CIRP Journal of Manufacturing Science and Technology,; Vol. 24(2019)p. 6-16
Ashfaq Farooqui, Petter Falkman, and Martin Fabian. Towards Auto- matic Learning of Discrete-Event Models using Queries and Observations.
Virtuell beredning och idrifttagning av produktionssystem inklusive PLC-logik
VINNOVA, 2014-07-01 -- 2017-06-30.
Engineering Tool Chain for Efficient and Iterative Development of Smart Factories (ENTOC)
VINNOVA, 2016-09-01 -- 2019-08-31.
Systematisk testning av cyberfysiska system (SyTeC)
Vetenskapsrådet (VR), 2017-01-01 -- 2022-12-31.
Chalmers tekniska högskola
Opponent: Christian Berger, Associate professor, Department of Computer Science and Engineering, University of Gothenburg, Sweden.