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UK Workshop on Computational Intelligence

UKCI 2019: Advances in Computational Intelligence Systems pp 483–492Cite as

Vehicle Warranty Claim Prediction from Diagnostic Data Using Classification

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1043))

Abstract

This paper presents an approach to predict warranty repair claims on automotive units based on joint on-board diagnostic and historic warranty repair data. The problem is framed as binary classification, facilitating the applicability of a variety of machine learning techniques. The approach allows automotive manufacturers to make better use of the operational and failure data collected from the field, allowing for better spend forecast and more targeted vehicle health management interventions and campaigns. The research evaluates the performance of Support Vector Machines, Random Forests and Decision Trees on the data set thus obtained is evaluated and the results are presented, highlighting the importance of hyper-parameter tuning for the problem considered. It is shown that the modelling methods employed demonstrate comparable performance, however the Decision Tree approach seems to perform the most consistently across the various target failure codes considered at this time.

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Acknowledgments

The research presented in this paper is funded by the Intelligent Personalised Powertrain Health Care research project, in collaboration with Jaguar Land Rover. The authors would like to thank the anonymous reviewers for their valuable feedback.

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Authors and Affiliations

  1. University of Bradford, Richmond Road, Bradford, BD7 1DP, UK

    Denis Torgunov, Paul Trundle, Felician Campean, Daniel Neagu & Andrew Sherratt

Authors
  1. Denis Torgunov
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  2. Paul Trundle
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  3. Felician Campean
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  4. Daniel Neagu
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  5. Andrew Sherratt
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Corresponding author

Correspondence to Denis Torgunov .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  2. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  3. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

    Chenguang Yang

  4. School of Computing, University of Portsmouth, Portsmouth, Hampshire, UK

    Alexander Gegov

  5. School of Computing, University of Portsmouth, Portsmouth, UK

    Dalin Zhou

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Cite this paper

Torgunov, D., Trundle, P., Campean, F., Neagu, D., Sherratt, A. (2020). Vehicle Warranty Claim Prediction from Diagnostic Data Using Classification. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_40

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