Project

RACKET

Rare Class Learning and Unknown Events Detection for Flexible Production

Rare Class Learning and Unknown Events Detection for Flexible Production

The RACKET project addresses the problem of detecting rare and unknown faults by combining model-based and machine learning methods. The approach is based on the assumption that a physical or procedural model of a manufacturing plant is available, which is not fully specified and has uncertainties in structure, parameters and variables. Gaps and errors in this model are detected by machine learning and corrected, resulting in a more realistic process model (nominal model). This model can be used to simulate system behavior and estimate the future characteristics of a product.

Actual product defects can thus be attributed to anomalies in the output signal and to inconsistencies in the process variables, without the need for a known failure event or an accurate failure model. Errors have a wide range, i.e., geometric errors such as scratches, out-of-tolerance dimensional variables, or dynamic errors such as deviations between estimated and actual product position on a conveyor belt, process steps or incorrect path assignment in the production flow, etc., and can occur at the product and process level.

Sponsors

Federal Ministry of Education and Research (BMBF)

Federal Ministry of Education and Research (BMBF)

Publications about the project

Yaxu Xie, Fangwen Shu, Alain Pagani, Jason Raphael Rambach, Didier Stricker

In: British Machine Vision Conference. British Machine Vision Conference (BMVC-2021) November 22-25 United Kingdom British Machine Vision Conference 11/2021.

To the publication
Yaxu Xie, Jason Raphael Rambach, Fangwen Shu, Didier Stricker

In: IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2021) May 30-June 5 Xi'an China IEEE 2021.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz