Business Area Distributed Data Processing & Control

Adaptive systems will be increasingly used in the future to assist people in the solving of complex tasks and problems. In addition, they help companies avoid errors and use resources in a particularly efficient way. The necessary condition for adaptive systems is the targeted use of sensor data, which is usually collected by the systems in large quantities.

We develop solutions that allow these volumes of diverse data to be processed and interpreted in a way that is particularly efficient and features a large element of self-learning. This leads to optimum strategies for the control of systems.

Our approach is based on the integration of expert knowledge, multi-physical modeling and state-of-the-art methods of data-based system analysis and machine learning.

The results of our work are used in a broad range of industrial fields − from quality management for industrial processes, to learning systems for condition monitoring and predictive maintenance, to the measurement-data-based management of wireless networks, to the optimization of energy systems in buildings and production facilities.

 

 

Industrial Data Analysis

Unplanned downtimes or unnecessary component exchanges create preventable costs and reduce productivity. Fraunhofer IIS/EAS developed an Condition Monitoring approach for an intelligent and independent monitoring of system components. The quick and user-friendly solution saves time, effort and thereby costs as well.

 

Wireless-networked Automation

In automation, wireless communication is more flexible and often more cost-effective than wired solutions. Wireless technologies can work just as reliably in this setting. However, for this to occur, interferences during transmission must be avoided. This is why we support you to plan and operate such systems securely – or to also analyze existing disturbances.

 

Energy Management

Even today inefficient energy systems still waste resources in many areas. We have therefore developed methods and procedures which can recognize and exploit this potential.

Project Examples

corona.KEX.net

The project pursued the development of an early-detection and prediction system for the medical sector. The aim was to identify supply bottlenecks early and take countermeasures in good time. This should ensure the supply of necessary items and, overall, make supply chains resilient.

FMI4BIM

A major challenge in managing decentralized energy systems in buildings is reconciling the different energy sources as well as the energy conversion and storage methods in use. The ARCHE results should simplify the planning and deployment of self-optimizing control architectures.

KISSS

Automated welding processes play a major role in many shipyards, but they reach their limits when it comes to individual components or production deviations. Artificial intelligence is intended to optimize these processes.

ACME 4.0

Fraunhofer IIS/EAS, together with its partners, developed a highly integrated, easy-to-use, flexible and affordable platform with autonomous energy supply for acoustic monitoring in industrial applications. The aim was to utilize airborne sound and ultrasound for multi-faceted tasks.

 

ARCHE

A major challenge in managing decentralized energy systems in buildings is reconciling the different energy sources as well as the energy conversion and storage methods in use. The ARCHE results should simplify the planning and deployment of self-optimizing control architectures.

Veritas

Digitization and increased flexibility in production promise to deliver efficiency and productivity gains. A key process in this development is automatic interaction between machines. The goal of the VERITAS project is to explore solutions for simple, fail-proof development of such systems.