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Towards relation discovery for diagnostics
Volvo Technology, Göteborg, Sweden.ORCID-id: 0000-0001-8255-1276
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), 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).
2011 (engelsk)Inngår i: Proceedings of the First International Workshop on Data Mining for Service and Maintenance, New York, NY: Association for Computing Machinery (ACM), 2011, s. 23-27Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

It is difficult to implement predictive maintenance in the automotive industry as it looks today, since the sensor capabilities and engineering effort available for diagnostic purposes is limited. It is, in practice, impossible to develop diagnostic algorithms capable of detecting many different kinds of faults that would be applicable to a wide range of vehicle configurations and usage patterns. However, it is now becoming feasible to obtain and analyse on-board data on vehicles as they are being used. It makes automatic data-mining methods an attractive alternative, since they are capable of adapting themselves to specific vehicle configurations and usage. In order to be useful, though, such methods need to be able to detect interesting relations between a large number of available signals. This paper presents an unsupervised method for discovering useful relations between measured signals in a Volvo truck, both during normal operations and when a fault has occurred. The interesting relationships are found in a two-step procedure. In the first step, we identify a set of “good” models, by establishing an MSE threshold over the complete data set. In the second step, we estimate model parameters over time, in order to capture the dynamic behaviour of the system. We use two different approaches here, the LASSO method and the Recursive Least Squares filter. The usefulness of obtained relations is then evaluated using supervised learning to separate different classes of faults.

sted, utgiver, år, opplag, sider
New York, NY: Association for Computing Machinery (ACM), 2011. s. 23-27
Emneord [en]
Fault detection, Machine learning, Vehicle diagnostics
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-15995DOI: 10.1145/2018673.2018678Scopus ID: 2-s2.0-80052625970ISBN: 978-145030842-7 OAI: oai:DiVA.org:hh-15995DiVA, id: diva2:437123
Konferanse
17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Tilgjengelig fra: 2011-09-07 Laget: 2011-08-26 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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