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Analysis of Truck Compressor Failures Based on Logged Vehicle Data
Volvo Group Trucks Technology, Advanced Technology & Research, Göteborg, Sweden.ORCID-id: 0000-0001-8255-1276
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0001-5163-2997
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
2013 (engelsk)Inngår i: / [ed] Hamid Reza Arabnia, CSREA Press, 2013Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In multiple industries, including automotive one, predictive maintenance is becoming more and more important, especially since the focus shifts from product to service-based operation. It requires, among other, being able to provide customers with uptime guarantees. It is natural to investigate the use of data mining techniques, especially since the same shift of focus, as well as technological advancements in the telecommunication solutions, makes long-term data collection more widespread.

In this paper we describe our experiences in predicting compressor faults using data that is logged on-board Volvo trucks. We discuss unique challenges that are posed by the specifics of the automotive domain. We show that predictive maintenance is possible and can result in significant cost savings, despite the relatively low amount of data available. We also discuss some of the problems we have encountered by employing out-of-the-box machine learning solutions, and identify areas where our task diverges from common assumptions underlying the majority of data mining research.

sted, utgiver, år, opplag, sider
CSREA Press, 2013.
Emneord [en]
Data Mining, Machine Learning, Fault Prediction, Automotive Diagnostics, Logged Vehicle Data
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-23457ISBN: 1-60132-235-6 (tryckt)OAI: oai:DiVA.org:hh-23457DiVA, id: diva2:644582
Konferanse
9th International Conference on Data Mining, Las Vegas, Nevada, USA, July 22–25, 2013
Forskningsfinansiär
VINNOVATilgjengelig fra: 2013-08-31 Laget: 2013-08-31 Sist oppdatert: 2018-03-22bibliografisk kontrollert
Inngår i avhandling
1. Machine learning methods for vehicle predictive maintenance using off-board and on-board data
Åpne denne publikasjonen i ny fane eller vindu >>Machine learning methods for vehicle predictive maintenance using off-board and on-board data
2014 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Vehicle uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. Traditional Fleet Management Systems are gradually extended with new features to improve reliability, such as better maintenance planning. Typical diagnostic and predictive maintenance methods require extensive experimentation and modelling during development. This is unfeasible if the complete vehicle is addressed as it would require too much engineering resources.

This thesis investigates unsupervised and supervised methods for predicting vehicle maintenance. The methods are data driven and use extensive amounts of data, either streamed, on-board data or historic and aggregated data from off-board databases. The methods rely on a telematics gateway that enables vehicles to communicate with a back-office system. Data representations, either aggregations or models, are sent wirelessly to an off-board system which analyses the data for deviations. These are later associated to the repair history and form a knowledge base that can be used to predict upcoming failures on other vehicles that show the same deviations.

The thesis further investigates different ways of doing data representations and deviation detection. The first one presented, COSMO, is an unsupervised and self-organised approach demonstrated on a fleet of city buses. It automatically comes up with the most interesting on-board data representations and uses a consensus based approach to isolate the deviating vehicle. The second approach outlined is a super-vised classification based on earlier collected and aggregated vehicle statistics in which the repair history is used to label the usage statistics. A classifier is trained to learn patterns in the usage data that precede specific repairs and thus can be used to predict vehicle maintenance. This method is demonstrated for failures of the vehicle air compressor and based on AB Volvo’s database of vehicle usage statistics.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University Press, 2014. s. 96
Serie
Halmstad University Dissertations ; 9
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-27869 (URN)978-91-87045-18-9 (ISBN)978-91-87045-17-2 (ISBN)
Presentation
2014-09-26, Haldasalen, Visionen, Halmstad University, Halmstad, 10:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
VINNOVA
Tilgjengelig fra: 2015-03-12 Laget: 2015-02-19 Sist oppdatert: 2015-03-17bibliografisk kontrollert

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