The availability of systems is one of the highest goals of any plant operator. Increased availability means the current status of each sensor and actuator assess about the product to the entire system and before or in the case of failure to perform error compensation measures to maintain the system operation. At the same time the required product quality shall be achieved in all situations. This is far more than nowadays diagnostic systems and asset management systems (AMS) achieve and requires new and innovative methods, based on current and future trends in technology. Especially the heterogeneity and complexity of data that needs to be processed (keyword Big Data) is one of the central challenges.
The Institute of Automation and Information Systems researches new and innovative methods for knowledge-based asset management systems, data-driven diagnostic methods in the context of Industry 4.0 and Cyber-Physical Production Systems (CPPS) in the complete plant lifecycle, and taking into account human factors when using human-machine interfaces.
AIValve - Self-learning and self-optimizing control of valves and valve systems for hydraulic machines and aggregates
Diagnose und Visualisierung - Research focus of reusable methods of diagnosis by analysis of aggregated data system for plant and machinery families
InSeLDiP - Intensification of separation processes in the food industry through digital twins and intelligent process control
M@OK - Online Echtzeit-Wissensmanagement, Data-Mining und Machine-Learning für den Maschinen- und Anlagenbau
SIDAP - Skalierbares Integrationskonzept zur Datenaggregation, -analyse, -aufbereitung von großen Datenmengen in der Prozessindustrie