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

conference contribution
posted on 06.12.2017, 00:00 by G Kahandawa, T Choudhury, M Ibrahim, P Dzitac, Abdul Mazid
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 in put parameters. The test results are analyzed and discussed in this paper.

History

Start Page

1729

End Page

1734

Number of Pages

6

Start Date

01/01/2015

Finish Date

01/01/2015

ISBN-13

9781479977994

Location

Seville, Spain

Publisher

IEEE

Place of Publication

Piscataway, NJ.

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Federation University Australia; School of Engineering and Technology (2013- ); TBA Research Institute;

Era Eligible

Yes

Name of Conference

IEEE International Conference on Industrial Technology