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Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain
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: 0000-0002-7796-5201
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
Aftermarket Solutions Department, Volvo Trucks, Gothenburg, Sweden.
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2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, New York, NY: Association for Computing Machinery (ACM), 2019, article id 4Conference paper, Published paper (Refereed)
Abstract [en]

Predicting future maintenance needs of equipment can be addressed in a variety of ways. Methods based on machine learning approaches provide an interesting platform for mining large data sets to find patterns that might correlate with a given fault. In this paper, we approach predictive maintenance as a classification problem and use Random Forest to separate data readouts within a particular time window into those corresponding to faulty and non-faulty component categories. We utilize diagnostic trouble codes (DTCs) as an example of event-based data, and propose four categories of features that can be derived from DTCs as a predictive maintenance framework. We test the approach using large-scale data from a fleet of heavy duty trucks, and show that DTCs can be used within our framework as indicators of imminent failures in different components.

Place, publisher, year, edition, pages
New York, NY: Association for Computing Machinery (ACM), 2019. article id 4
Keywords [en]
Predictive maintenance, failure detection, diagnostic trouble codes, feature extraction
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-40184DOI: 10.1145/3304079.3310288ISBN: 978-1-4503-6296-2 (electronic)OAI: oai:DiVA.org:hh-40184DiVA, id: diva2:1335754
Conference
WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11-15 February, 2019
Available from: 2019-07-07 Created: 2019-07-07 Last updated: 2019-08-02Bibliographically approved

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Pirasteh, ParivashNowaczyk, SławomirPashami, SepidehBerck, Peter
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