Data Analytics: AI for data-driven system optimization

The Data Analytics group develops innovative AI-based solutions to get the most out of data generated in the context of smart power electronics and Industry 4.0. The basis for this is an application-oriented approach that includes system analysis, design, data acquisition, filtering, clustering, and finally the development and implementation of intelligent algorithms in embedded systems or in industrial processes. Important for viable solutions in the dynamic field of data analytics and AI is a close collaboration with relevant teams and institutions, e.g. the Modeling and Artificial Intelligence department of IISB or the ADA Lovelace Center, of which Fraunhofer IISB is a founding member.

From Fraunhofer IISB's core topic Power Electronic Systems, two main areas for the application of data analytics solutions were chosen:

  • Cognitive Power Electronics 4.0: Intelligent Power Electronics
  • Dr. Production®: Intelligent Manufacturing Equipment

Cognitive Power Electronics 4.0: Intelligent Power Electronics

Well established power electronic system technology combined with new, intelligent functionalities.

In the new research area Cognitive Power Electronics 4.0 (CPE4.0), Fraunhofer IISB combines its core competence in the field of power electronic systems with data analytics and artificial intelligence. This integration enables innovative applications for intelligent power electronics: from data acquisition and analysis in the converter to predictive maintenance in interaction with the cloud.

Cognitive Power Electronics 4.0 is made possible by linking data knowledge with system knowledge within Fraunhofer IISB: Expertise in the field of power electronics regarding the conversion, supply and storage of electrical energy is incorporated into the intelligent functionalities. Examples of current developments are inverter-based health monitoring functions for electric drives without additional sensors, converter-based impedance spectroscopy and stability optimization in DC networks.

Use Case of CPE4.0: Intelligente Drive Technology

One use case of CPE4.0 is electrical drive technology, where the drive inverter is modified and the electrical parameters of the inverter are used for predictive analysis. The electric drive thereby becomes an integrated intelligent system that can provide information about its current and future operating state and can also (re-)act independently.

© Fraunhofer IISB

Use Case of CPE4.0: Intelligent DC grids

Many renewable energy sources supply electrical energy in direct current. Energy storage systems and consumers are also mostly DC-based. By intelligently interconnecting them in DC grids, AC-to-DC conversions are reduced, saving energy. AI-based solutions for smart DC grids are based on learning with sparsely annotated data, sequence-based learning, and mathematical optimization.

© Fraunhofer IIS

Dr. Production®: Intelligent Manufacturing Equipment

Practical design and implementation of Industry 4.0 solutions.

Dr. Production® comprises a toolbox of methods and algorithms that has been developed over two decades of research on automated process control in semiconductor manufacturing. It consists of three modules for application:

  1. Application-oriented consulting and conception
  2. Analysis of manufacturing processes and data acquisition
  3. Development and implementation of intelligent algorithms

Depending on the requirements, all or only selected modules can be run through to develop a customer-specific solution. The diverse optimization options include, for example, condition monitoring of equipment, fault detection and classification, or predictive methods such as predictive maintenance and virtual metrology.

Use Case of Dr. Production®: Use Case 10 of the “Integrated Development 4.0 (iDev4.0)” Project

In cooperation with the Data Analytics group, Fraunhofer IISB's Semiconductor Devices department has worked on Use Case 10: "Smart Platform for Rapid Prototyping of Low-Volume Devices" of the iDev4.0 project. Based on various parameters that are already known before processing, prediction models can be developed even with a small amount of data. In the context of Use Case 10, these are etch rate predictions that enable intelligent rapid prototyping

© Fraunhofer IISB