New artificial intelligence based tire size identification for fast and safe inflating cycle

Gayan Kahandawa, T. A. Choudhury, M. Yousef Ibrahim, Pavel Dzitac, Abdul Md Mazid

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

Abstract

Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Industrial Technology, ICIT 2015
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1729-1734
Number of pages6
ISBN (Electronic)9781479978007, 9781479977994
ISBN (Print)9781479978014
DOIs
Publication statusPublished - 16 Jun 2015
Externally publishedYes
Event2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Spain
Duration: 17 Mar 201519 Mar 2015

Other

Other2015 IEEE International Conference on Industrial Technology, ICIT 2015
CountrySpain
CitySeville
Period17/03/1519/03/15

Keywords

  • artificial neural network
  • back-propagation algorithm
  • sensors
  • tire safety
  • microcontroller applications
  • PRESSURE

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