VIMA: Modeling and Visualization of High Dimensional Machine Sensor Data Leveraging Multiple Sources of Domain Knowledge

Joscha Eirich, Dominik Jäckle, Tobias Schreck, Jakob Bonart, Oliver Posegga, Kai Fischbach

External link (DOI)
View presentation:2020-10-26T15:15:00ZGMT-0600Change your timezone on the schedule page
2020-10-26T15:15:00Z
Exemplar figure
The process of building machine learning models in a real-time manufacturing environment. Machine-sensor data is sent
Keywords

Abstract

The highly integrated design of the electrified power train creates new challenges in the holistic testing of high-quality standards. Test technicians face the challenge that tests for such new technologies are just about to be developed. Thus, they cannot rely on their gut feeling, but require automated support, which is not yet available. We present VIMA, a system that processes high dimensional machine-sensor data to support test technicians with their analyses of produced parts and to interactively create labels. We demonstrate the usefulness of VIMA in a qualitative user study with four test technicians. The results indicate that VIMA helps to identify abnormal parts, that were not detected by the established testing procedures. Additionally, we use the labels, generated interactively through VIMA, to deploy a model running on a test station in a real manufacturing environment; the model outperforms the current testing procedure in detecting increased backlashes of electrical engines.