Implementing K-Star Algorithm to Monitor Tyre Pressure using Extracted Statistical Features from Vertical Wheel Hub Vibrations

Abstract

Objectives: Tyre pressure monitoring systems are automotive electronic systems used to monitor the automobile tyre pressure. The existing systems use pressure sensors or wheel speed sensors. They depend on batteries and radio transmitters which would add up to cost and complexity. Methods/Analysis: This paper proposes a new machine learning approach to monitor the tyre pressure. Vertical vibrations are extracted from a wheel hub of a moving vehicle using an accelerometer and are classified using machine learning techniques. The statistical features are extracted from the vibration signal and the features are classified using K Star algorithm. Findings: A reasonably high classification accuracy of 89.16% was obtained. Application/Improvements: The proposed model can be used for monitoring the automobile tyre pressure successfully.