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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

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%.
Originalspracheenglisch
Titel2020 IEEE Visualization in Data Science (VDS)
Herausgeber (Verlag)IEEE Press
Seiten22-31
Seitenumfang10
ISBN (elektronisch)978-1-7281-9284-0
DOIs
PublikationsstatusVeröffentlicht - Okt 2020
VeranstaltungIEEE VIS 2020 - Virtuell, USA / Vereinigte Staaten
Dauer: 25 Okt 202030 Okt 2020
http://ieeevis.org/year/2020/welcome

Konferenz

KonferenzIEEE VIS 2020
KurztitelVIS 2020
LandUSA / Vereinigte Staaten
OrtVirtuell
Zeitraum25/10/2030/10/20
Internetadresse

ASJC Scopus subject areas

  • !!Computer Science Applications
  • !!Media Technology

Fields of Expertise

  • Information, Communication & Computing

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