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Indirect Tire Monitoring System - Machine Learning Approach
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology.
2017 (English)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The heavy duty vehicle industry has today no requirement to providea tire pressure monitoring system by law. This has created issues sur-rounding unknown tire pressure and thread depth during active service.There is also no standardization for these kind of systems which meansthat different manufacturers and third party solutions work after theirown principles and it can be hard to know what works for a given vehicletype. National Highway Traffic Safety Administration (NHTSA) put out a new study that determined that underinflated tires of 25% or less are 3 times more likely to be involved in a crash related to tire issues versus vehicles with properly inflated tires.

The objective for this thesis 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. Drivec Bridge hardware interprets existing sensors from the vehicle.

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. The classifier will be implemented in Drivecs cloud service and use data to classify  the tires condition. The resulting classifier of the project is a random forest implemented in Python. The result from the front axle with a dataset consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of90.54% (±0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017.

The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. The project group has verified with high accuracy that a vehicle has been classified as bad and then been reclassified as good over a time span of 16 days. At the first day offboard measurements were performed and it showed that the tires of the front axle were underinflated. The classifier indicated that the vehicle had bad classifications until day 14. At this day an offboard measurement was performed and it was concluded that they were no longer underinflated and the classifier indicated this as well. To verify the result the workshop was contacted and verified that the vehicle had changed tires of the front axle at day 14. This has verified that the classifier is able to detect change and stay consistent in the results over a longer time period.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Machine Learning, Tire Monitoring, Fleet Management, Cloud, Internet of Things, Artificial Intelligence
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:hh:diva-34290OAI: oai:DiVA.org:hh-34290DiVA: diva2:1113511
External cooperation
Drivec AB
Subject / course
Computer science and engineering
Supervisors
Examiners
Available from: 2017-06-30 Created: 2017-06-21 Last updated: 2017-06-30Bibliographically approved

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CiteExportLink to record
Permanent link

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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