期刊
2020 IEEE VISUALIZATION IN DATA SCIENCE (VDS 2020)
卷 -, 期 -, 页码 22-31出版社
IEEE COMPUTER SOC
DOI: 10.1109/VDS51726.2020.00007
关键词
Visual analytics; machine learning; knowledge creation; interactive labeling; anomaly detection
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%.
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