Engineering Methods and Data Management for Mobile and Stationary Mechatronic Systems

The lecture "Engineering Methods and Data Management for Mobile and Stationary Mechatronic Systems" builds on the fundamentals of the lecture "Industrial Automation 1" and focuses on the detailed planning and implementation of mobile and stationary mechatronic systems. Current research topics and methods are presented that are used from the development to the operation phase of automation systems. In addition to supporting the interdisciplinary design of mobile and stationary mechatronic systems, model-based methods for analyzing control software are considered. Furthermore, communication architectures and current communication standards (e.g. OPC-UA) with special focus on the real-time requirements of automation systems are content of the lecture. The methods and approaches of BigData for handling and analyzing large amounts of data are taught and their visualization by means of modern visualization methods, such as augmented reality and 3D visualization, is presented, which are intended to support humans in understanding and operating the increasingly complex systems (e.g. by means of training systems).

After participating in the lecture, students will be able to understand the development and implementation of mobile and stationary mechatronic systems and to evaluate and apply the necessary methods.The students are able to classify methods of software engineering in the form of modeling and notations (SysML, AML) for the structure, as well as the behavior of software programs and apply them to given tasks or develop appropriate models to describe the problem. By considering the different levels of automation technology, students are able to differentiate the individual systems and understand their interdependencies. The students know methods of data management and data mining and can apply them to practical examples. Here, special emphasis is placed on ensuring that students acquire a stable foundation from data collection, through necessary data pre-processing algorithms, to complete data analysis, in order to lay a good foundation for data understanding and quality. In combination with the fundamentals of latency behavior in distributed cyber-physical systems, which are also covered, students should thus also be able to evaluate complete data pipelines. Furthermore, they will be able to understand, analyze and evaluate visualization concepts for human-machine interfaces.