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Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
Volvo Group Trucks Technology, Gothenburg, 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).
2015 (engelsk)Inngår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 41, s. 139-150Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.

Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. © 2015 Elsevier Ltd.

sted, utgiver, år, opplag, sider
Oxford: Pergamon Press, 2015. Vol. 41, s. 139-150
Emneord [en]
Machine Learning, Diagnostics, Fault Detection, Automotive Industry, Air Compressor
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-27808DOI: 10.1016/j.engappai.2015.02.009ISI: 000353739800012Scopus ID: 2-s2.0-84926374379OAI: oai:DiVA.org:hh-27808DiVA, id: diva2:788708
Prosjekter
in4uptime
Forskningsfinansiär
VINNOVAKnowledge Foundation
Merknad

The authors thank Vinnova (Swedish Governmental Agency for Innovation Systems), AB Volvo, Halmstad University, and the Swedish Knowledge Foundation for financial support for doing this research.

Tilgjengelig fra: 2015-02-16 Laget: 2015-02-16 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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