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Automatic Defect Detection by One-Class Classification on Raw Vehicle Sensor Data

  • Julia Hofmockel
  • Felix Richter
  • Eric Sax
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10352)

Abstract

The next step in the automotive industry is the automatic detection of a defect in the vehicle behavior in addition to the current analysis of failure codes or costumer complaints. The idea of learning the normality by one-class classification is applied to the identification of an exemplary defect. Different neural network topologies for time series prediction are realized where the quality of the forecast indicates the strength of abnormality. It is compared how the detection possibilities of a concrete defect changes when the model is trained with different data extractions. A distinction is made between data from complete rides and filtered data, containing only the situations where the defect is visible. It can be shown that a generalization is possible.

Keywords

Anomaly detection One-class classification Time series prediction 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Audi Electronics Venture GmbHGaimersheimGermany
  2. 2.Volkswagen AGWolfsburgGermany
  3. 3.Institute for Information Processing Technologies, Karlsruher Institute of Technology, KITKarlsruheGermany

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