hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Indirect Tire Monitoring System - Machine Learning Approach
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3034-6630
2017 (English)In: IOP Conference Series: Materials Science and Engineering, Bristol: Institute of Physics Publishing (IOPP), 2017, Vol. 252, article id 012018Conference paper, Published paper (Refereed)
Abstract [en]

The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an acceptable range over a period of time. These incidents will be notified through the cloud for the operator to analyze for upcoming maintenance decisions. © 2017 Published under licence by IOP Publishing Ltd.

Place, publisher, year, edition, pages
Bristol: Institute of Physics Publishing (IOPP), 2017. Vol. 252, article id 012018
Series
IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, E-ISSN 1757-899X ; 252
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-35499DOI: 10.1088/1757-899X/252/1/012018Scopus ID: 2-s2.0-85034218557OAI: oai:DiVA.org:hh-35499DiVA, id: diva2:1161107
Conference
11th International Congress of Automotive and Transport Engineering: Mobility Engineering and Environment (CAR 2017), Pitesti, Romania, 8-10 November, 2017
Available from: 2017-11-29 Created: 2017-11-29 Last updated: 2017-12-11Bibliographically approved

Open Access in DiVA

fulltext(489 kB)19 downloads
File information
File name FULLTEXT01.pdfFile size 489 kBChecksum SHA-512
7ae72b1c81a2edf0f4a859dda39a27f5cae14cfac286f363ab606c6a3478d3fe0df7a99100de2461347fd8cf89bba790643898db0298112ff0b11b8fbd2f263e
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Byttner, StefanFan, Yuantao

Search in DiVA

By author/editor
Byttner, StefanFan, Yuantao
By organisation
School of Information TechnologyCAISR - Center for Applied Intelligent Systems Research
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 19 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 84 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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