Tyre condition monitoring system (TCMS) is an emerging electronic safety system present in most of the new generation vehicles. It is intended to warn the driver when wheels are unbalanced or inflation pressure in a tyre goes below a certain recommended value. In India only limited vehicles having this critical safety system installed because it is highly expensive. The existing TCMS monitor only the tyre pressure using direct or indirect methods. In recent days the indirect TCMS using vibration signals and machine learning techniques are gaining momentum. This paper reports the effect of wheel balancing on a proposed affordable indirect TCMS. The present study goes one step forward to include the wheel balancing in the TCMS, thus increasing the scope of TCMS. The study was carried out using vibration signal data acquired from a low cost accelerometer placed on the wheel hub. The features required were taken out from the acquired data with the help of statistical feature extraction techniques. Features selection and feature classification were processed with J48 decision tree algorithm. The effect wheel balancing in classification accuracy is clearly explained.
Tyre condition monitoring systems (TCMS) are the safety systems used in a vehicle for measuring the condition of tyre like tyre pressure, temperature, balancing etc. In this era of increasing vehicle accidents, these systems are having paramount importance in terms of safety. The current technology TCMS uses direct sensors like pressure sensors or wheel speed sensors etc, which are highly expensive. This paper puts forward an innovative indirect TCMS system using condition monitoring techniques and machine learning. For different five balancing conditions vertical vibrations from fixed wheel hub were extracted from a moving air filled tyre with the help of an accelerometer. The tyres filled with air are considered with different pressure values to represent puncture, normal, idle and high pressure conditions. In feature extraction process, the statistical features were extracted from the acquired signals and the prominent features were selected using J48 algorithm. Selected features were classified with the help of classification via regression algorithm and reasonable high accuracy was obtained. This paper attempts to study the effect of unbalance of the wheel on the classification accuracy of an indirect TPMS system. The results are compared and presented
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