München-Garching  /  May 17, 2022

My Van Hoai will give a presentation on the topic »Framework for Data and AI Lifecycle«

The event on May 17, 2022 was cancelled at short notice.

Artificial intelligence (AI) in industry has a lot of potential. Production and services can be constantly analyzed and improved. Fraunhofer IKS presents a framework for the data and AI lifecycle that exploits this potential. An example "predictive maintenance of a production line" will be used to illustrate the framework.

Hoai My Van, researcher at Fraunhofer IKS since October 2020, will hold a lecture on May 17 at 11:00 a.m. at the Science Congress Center Munich-Garching. The conference will take place in presence.

Hoai My Van studied electrical engineering and information technology at TU Munich with a focus on Machine Intelligence. At Fraunhofer IKS, she conducts research on flexible and adaptive architectures, especially for artificial intelligence.


Artificial Intelligence in Industry 4.0

Flexible workflows in automated industrial systems greatly increase the complexity. Conventional analysis and optimization methods run up against their limits in these environments.

By relying on machine learning methods and so-called data mining, the data from cyber-physical systems can be used to create a flexible finite state machine, which is an artificial intelligence (AI)-based model that not only represents a copy of the behavior, but the framework of the normal behavior.

With these finite state machines that represent process chains, contexts and dependencies that are too complex to identify with other methods become visible. With this approach, not only can the time behavior of individual machines can be observed, but the interactive behavior of entire systems. This digital counterpart of a physical system or machine is often described as a »digital twin«. More than just a copy of the previous behavior, it serves as a digital representation that accompanies the entire actual life cycle.

A monitoring instance then uses this model to observe the real system and analyze the entire production process. Latent behavior patterns that are learned from the data form the basis for comprehensive optimization and process automation.