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Warranty Claim Rate Prediction using Logged Vehicle Data
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3272-4145
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
2019 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 11804, p. 663-674Article in journal (Refereed) Published
Abstract [en]

Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of the quality of the product leads to better designs in the future, and better maintenance to solve the current issues. To this end, the integration of large amounts of data that are logged during the vehicle operation can be used to build the model of usage patterns for early prediction. In this study, we have developed a machine learning system for warranty claims by integrating available information sources: Logged Vehicle Data (LVD) and Warranty Claims (WCs). The experimental results obtained from a large data set of heavy duty trucks are used to demonstrate the effectiveness of the proposed system to predict the warranty claims. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Heidelberg: Springer, 2019. Vol. 11804, p. 663-674
Keywords [en]
Warranty Claim Predictive, Machine Learning, Fault De- tection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-41207DOI: 10.1007/978-3-030-30241-2_55Scopus ID: 2-s2.0-85072879357OAI: oai:DiVA.org:hh-41207DiVA, id: diva2:1376729
Conference
19th EPIA Conference on Artificial Intelligence (EPIA 2019), Vila Real, Portugal, 3-6 September, 2019
Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2019-12-11Bibliographically approved

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Khoshkangini, RezaPashami, SepidehNowaczyk, Sławomir

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