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Machine learning methods for vehicle predictive maintenance using off-board and on-board data
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Volvo Group Trucks Technology, Malmö, Sweden.ORCID-id: 0000-0001-8255-1276
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: urn:nbn:se:hh:diva-27869Libris ID: 17734466ISBN: 978-91-87045-18-9 ISBN: 978-91-87045-17-2 OAI: oai:DiVA.org:hh-27869DiVA, id: diva2:789498
Presentation
2014-09-26, Haldasalen, Visionen, Halmstad University, Halmstad, 10:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
VINNOVATilgjengelig fra: 2015-03-12 Laget: 2015-02-19 Sist oppdatert: 2015-03-17bibliografisk kontrollert
Delarbeid
1. Towards relation discovery for diagnostics
Åpne denne publikasjonen i ny fane eller vindu >>Towards relation discovery for diagnostics
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
Emneord
Fault detection, Machine learning, Vehicle diagnostics
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-15995 (URN)10.1145/2018673.2018678 (DOI)2-s2.0-80052625970 (Scopus ID)978-145030842-7 (ISBN)
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
2. Wisdom of Crowds for Intelligent Monitoring of Vehicle Fleets
Åpne denne publikasjonen i ny fane eller vindu >>Wisdom of Crowds for Intelligent Monitoring of Vehicle Fleets
Vise andre…
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

An approach is presented and experimentally demonstrated where consensus among distributed self-organized agents is used for intelligent monitoring of mobile cyberphysical systems (in this case vehicles). The demonstration is done on test data from a 30 month long field test with a city bus fleet under real operating conditions. The self-organized models operate on-board the systems, like embedded agents, communicate their states over a wireless communication link, and their states are compared off-line to find systems that deviate from the consensus. In this way is the group (the fleet) of systems used to detect errors that actually occur. This can be used to build up a knowledge base that can be accumulated over the life-time of the systems.

Emneord
Fault diagnosis, learning systems, mechatronics, self-monitoring, intelligent transportation systems
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-27970 (URN)
Forskningsfinansiär
VINNOVASwedish Research Council
Tilgjengelig fra: 2015-03-10 Laget: 2015-03-10 Sist oppdatert: 2018-03-22bibliografisk kontrollert
3. Analysis of Truck Compressor Failures Based on Logged Vehicle Data
Åpne denne publikasjonen i ny fane eller vindu >>Analysis of Truck Compressor Failures Based on Logged Vehicle Data
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
Data Mining, Machine Learning, Fault Prediction, Automotive Diagnostics, Logged Vehicle Data
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-23457 (URN)1-60132-235-6 (ISBN)
Konferanse
9th International Conference on Data Mining, Las Vegas, Nevada, USA, July 22–25, 2013
Forskningsfinansiär
VINNOVA
Tilgjengelig fra: 2013-08-31 Laget: 2013-08-31 Sist oppdatert: 2018-03-22bibliografisk kontrollert
4. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
Åpne denne publikasjonen i ny fane eller vindu >>Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
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
Emneord
Machine Learning, Diagnostics, Fault Detection, Automotive Industry, Air Compressor
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-27808 (URN)10.1016/j.engappai.2015.02.009 (DOI)000353739800012 ()2-s2.0-84926374379 (Scopus ID)
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

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