hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Wisdom of Crowds for Intelligent Monitoring of Vehicle Fleets
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Volvo Group Trucks Technology, Göteborg, Sweden.ORCID iD: 0000-0001-8255-1276
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
Show others and affiliations
(English)Manuscript (preprint) (Other academic)
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.

Keyword [en]
Fault diagnosis, learning systems, mechatronics, self-monitoring, intelligent transportation systems
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-27970OAI: oai:DiVA.org:hh-27970DiVA: diva2:794271
Funder
VINNOVASwedish Research Council
Available from: 2015-03-10 Created: 2015-03-10 Last updated: 2017-06-02Bibliographically approved
In thesis
1. Machine learning methods for vehicle predictive maintenance using off-board and on-board data
Open this publication in new window or tab >>Machine learning methods for vehicle predictive maintenance using off-board and on-board data
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2014. 96 p.
Series
Halmstad University Dissertations, 9
National Category
Signal Processing
Identifiers
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 (English)
Opponent
Supervisors
Funder
VINNOVA
Available from: 2015-03-12 Created: 2015-02-19 Last updated: 2015-03-17Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Rögnvaldsson, ThorsteinnByttner, StefanPrytz, RuneNowaczyk, SławomirSvensson, Magnus
By organisation
CAISR - Center for Applied Intelligent Systems Research
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

Total: 946 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf