Open Access

ARTICLE

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

by Sujit S. Pardeshi¹, Abhishek D. Patange¹, R. Jegadeeshwaran^2,*, Mayur R. Bhosale³

1 Department of Mechanical Engineering, College of Engineering Pune, Maharashtra, 411005, India
2 School of Mechanical Engineering, Vellore Institute of Technology, Tamil Nadu, 600127, India
3 Savitribai Phule Pune University, Maharashtra, 411007, India

* Corresponding Author: R. Jegadeeshwaran. Email:

Structural Durability & Health Monitoring 2022, 16(3), 271-290. https://doi.org/10.32604/sdhm.2022.010622

Received 14 March 2020; Accepted 23 April 2020; Issue published 18 July 2022

Abstract

The regulation of tyre pressure is treated as a significant aspect of ‘tyre maintenance’ in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the same in near future. The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle. In order to examine the vibration response of a wheel hub, the in-house design & development of DAQ (Data Acquisition System) is described. Micro Electro-Mechanical Scheme (MEMS) built accelerometer is incorporated with open source hardware and software to collect and store the data. This framework is easy to develop, monitor and can be retrofitted in two wheeled vehicle. For various pressure conditions, the change in response of wheel hub vibration with respect to time is collected. The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters. The classification of different conditions of tyre pressure is carried out using ML classifiers.

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Keywords

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