VIMA: Modeling and Visualization of High Dimensional Machine Sensor Data Leveraging Multiple Sources of Domain Knowledge
Joscha Eirich, Dominik Jäckle, Tobias Schreck, J. A. E. Bonart, Oliver Posegga, Kai Fischbach · 2020
The highly integrated design of the electrified powertrain creates new challenges in the holistic testing of high-quality standards. Particularly test technicians face the challenge, that lots of machine sensor data is recorded during these tests that needs to be analyzed. We present VIMA, a VA system that processes high dimensional machine-sensor data to support test technicians with these analyses. VIMA makes use of the concept of interactive labeling to train machine learning models and the process model of knowledge creation in visual analytics to create new knowledge through the interaction with the system. Its usefulness is demonstrated in a qualitative user study with four test technicians. Results indicate that through VIMA, previously undetected abnormal parts, could be identified. Additionally, a model trained with labels generated through VIMA, was deployed on a test station, that outperforms the current testing procedure, in detecting increased backlashes and improved the test benches output by 15%.