PIAD: Physics-informed anomaly detection

• Duration: 01.11.2024 - 31.10.2027
  Funding Organization: BMBF
  
  Prof. Dr. Marius Kloft
  University of Kaiserslautern-Landau
  
  

  Motivation: In recent years, enormous progress has been made in the field of artificial intelligence (AI). New methods support people in their daily and professional tasks and make it possible to automate many processes that previously required strenuous manual work. A particularly important area of application for AI is anomaly detection, i.e. identifying data that deviates from the norm. Examples include mutations in virology, cases of fraud in finance or faulty components in industrial production. However, current anomaly detectors are limited to applications where enormous amounts of training data are available. This is precisely where the “PIAD” research project comes in.

  Aims and approach: The aim is to develop a new type of physically-informed AI for areas that do not have a large available database, such as the laser beam melting application pursued in the project. Physics-informed means that physical knowledge and rules are incorporated into the AI. This is based on state-of-the-art neural recognition algorithms, which have recently been able to drastically reduce error rates. Model architectures are being developed that specifically incorporate information from the data domain in order to further increase data efficiency. Methods are being developed to expand and improve the existing training data. The prototype application will be evaluated as an example in additive manufacturing, particularly in efficient process monitoring.

  Innovations and perspectives: The use of anomaly detection systems can significantly increase data efficiency and even prevent disasters, for example by detecting unusual heat developments in factories during production processes. A successful application in additive manufacturing would demonstrate the potential of this technology in practice. A successful project would also mean a breakthrough in basic research by making AI-supported anomaly detection flexible, resilient and practicable even in data-poor areas.

  
  
  https://www.softwaresysteme.dlr-pt.de/de/machine-learning-modelle.php
  
  

AI Focus Areas of the Research Project

• Basic Research

  • Machine Learning (ML): Unsupervised Learning, Anomaly Detection
  • Technology Analysis: Economic Effects, Human-AI Interaction



• Application related Research

  • Smart Assistant Systems: Smart Production
  • Autonomous Systems: Smart Automation
  • Robotics: Industrial Robots
  • Virtual and Augmented Reality (AR): Assistance Systems at the Workplace