Two-wheeler tyre pressure monitoring through K-nearest neighbours algorithm trained using wheel hub vibrations acquired using ADXL335 accelerometer Online publication date: Mon, 16-Jan-2023
by Keshav H. Jatakar; Gopal V. Mulgund; Abhishek D. Patange; Bhagyesh B. Deshmukh; Kishor S. Rambhad; Vednath P. Kalbande
International Journal of Vehicle Noise and Vibration (IJVNV), Vol. 18, No. 3/4, 2022
Abstract: Maintaining optimal tyre pressure enhances the performance of a vehicle in many ways. Tyre pressure monitoring system (TPMS) provides a safety feature that shows an alert when the car's tyre pressure drops below the recommended levels. In this paper, the TPMS is built through training of the KNN algorithm based on wheel hub vibrations. An inexpensive system was developed by interfacing the ADXL335 accelerometer with Arduino for collecting real-time data. In order to process the raw data suitable conditioning was undertaken. The initial judgment of wkNNheel hub vibrations was carried out statistically. The features reflecting the relevant statistical judgment of tyre pressure conditions were selected and training of the KNN algorithm was initiated. Perfectly filled, partially filled and unhealthy tyre conditions were considered while acquiring the wheel hub vibrations and classification was achieved.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Noise and Vibration (IJVNV):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook