Projects and products of the department MRD

The ResilVerbund project focuses on the AI-supported monitoring and forecasting of biodiversity and water quality in drinking water reservoirs.

Concepts and procedures for the robust localization and navigation of underwater vehicles and for the realization of the planned inspection and exploration capabilities are developed.

The Potential pilot AI is a compact, modular solution that offers medium-sized companies a cost-effective way to evaluate the potential of AI processes in their production processes.

In this cooperation project, an intelligent system is to be designed and implemented as a prototype that enables the rapid detection and precise localization of leaks in district heating networks based on data and, based on this, calculates response strategies for shutting off network sections while ensuring an optimal supply to end customers.

Understanding real traffic - safeguarding automated mobility. The safe introduction of highly and fully automated vehicles depends crucially on how well we can digitally map today's traffic events - especially critical situations. AVEAS develops scalable, data protection-compliant methods for systematically recording and modeling risk-relevant scenarios and transferring them to virtual test environments.

Das Projekt CC-King hat sich zum Ziel gesetzt, KI-Spitzenforschung mit etablierten Ingenieurdisziplinen zu verbinden. Hierdurch sollen KI-Methoden und maschinelle Lernverfahren (ML) entsprechend den typischen Anforderungen und Vorgehensweisen von Ingenieuren nutzbar gemacht werden.

In order to keep the total operating costs of wind turbines (WTGs) competitive, the risk of failure must be minimized, maintenance costs reduced, and system availability and energy efficiency increased. This goal is achieved by introducing the most efficient, automated multi-sensory online monitoring and diagnostic systems - so-called Condition Monitoring Systems (CMS) - whose economic importance is increasingly recognized by wind farm operators, manufacturers and insurers.

The W-Net 4.0 project aims to develop a modular and scalable platform that combines GIS system, simulation software and data analysis tools and meets high IT security standards. Combined with novel service concepts, value-added networks and training concepts, small and medium-sized water supply companies will be enabled to use these technologies for the first time. For large utilities, novel and easy-to-use data analysis and optimization tools as well as corresponding service concepts will be made available.

The RICE project was launched as part of the technology development program to develop tools that can collect plant data in a minimally invasive manner and actively intervene in processes. The long-term goal is to improve the efficiency of industrial manufacturing systems.

AI-supported recording and forecasting of biodiversity and water quality in drinking water reservoirs.