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A domain-specific decision support system for knowledge discovery using association and text mining

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Abstract

We propose a novel association and text mining system for knowledge discovery (ASTEK) from the warranty and service data in the automotive domain. The complex architecture of modern vehicles makes fault diagnosis and isolation a non-trivial task. The association mining isolates anomaly cases from the millions of service and claims records. ASTEK has shown 86% accuracy in correctly identifying the anomaly cases. The text mining subscribes to the diagnosis and prognosis (D&P) ontology, which provides the necessary domain-specific knowledge. The root causes associated with the anomaly cases are identified by discovering frequent symptoms associated with the part failures along with the repair actions used to fix the part failures. The best-practice knowledge is disseminated to the dealers involved in the anomaly cases. ASTEK has been implemented as a prototype in the service and quality department of GM and its performance has been validated in the real life set up. On an average, the analysis time is reduced from few weeks to few minutes, which in real life industry are significant improvements.

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Authors and Affiliations

  1. Diagnosis and Prognosis Group, India Science Lab, General Motors Global Research and Development, GM Technical Centre India Pvt. Ltd., Creator Building, International Technology Park, Whitefiled, Bangalore, 560066, Karnataka, India

    Dnyanesh Rajpathak, Rahul Chougule & Pulak Bandyopadhyay

Authors
  1. Dnyanesh Rajpathak
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  2. Rahul Chougule
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  3. Pulak Bandyopadhyay
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Correspondence to Dnyanesh Rajpathak.

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Rajpathak, D., Chougule, R. & Bandyopadhyay, P. A domain-specific decision support system for knowledge discovery using association and text mining. Knowl Inf Syst 31, 405–432 (2012). https://doi.org/10.1007/s10115-011-0409-1

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  • DOI: https://doi.org/10.1007/s10115-011-0409-1

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