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Erschienen in:
2023 | OriginalPaper | Buchkapitel
Topological Data Analysis in Automotive Industry
verfasst von : F. Beutenmüller, B. Dierolf, M. Keckeisen, F. Pausinger, P. K. S. Vaudrevange
Erschienen in: 23. Internationales Stuttgarter Symposium
Verlag: Springer Fachmedien Wiesbaden
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Abstract
The automotive industry is facing the challenge of gaining knowledge from large datasets—originating for example from the research and development process, IT systems, production, or from fleet data. Problems most likely become manifest in data and result in increased costs. Examples can range from problems in the on-board electrical system to virtual validation of autonomous driving functions in R&D.
Hence, one important use case is the automatic detection of anomalous behavior in the data to forecast and identify potential problems as early as possible.
We demonstrate new mathematical methods from Topological Data Analysis (TDA) that can help to address these kinds of problems. TDA is a rather new field in mathematics that combines techniques from geometry and topology to analyze noisy datasets. Beside academia, it has been applied successfully to various fields including medicine (identification of tumor cells), finance (fraud detection), and materials science (structure analysis).
We highlight two main methods from TDA: the (ball) mapper algorithm and persistent homology and illustrate potential applications in automotive industry. We illustrate these abstract methods and show that they can produce valuable knowledge about potential problems—for example in the automotive context—giving an added value to the customer and the OEM.