Open Access

ARTICLE

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

P. S. Anoop¹, V. Sugumaran²

1 Research Scholar, School of Mechanical and Building Sciences (SMBS), Vellore Institute of Technology Chennai campus, Chennai, India. Email: anoopps.vit@gmail.com
2 Associate Professor, School of Mechanical and Building Sciences (SMBS), Vellore Institute of Technology Chennai campus, Chennai, India. Email: v_sugu@yahoo.com

Structural Durability & Health Monitoring 2017, 11(2), 191-208. https://doi.org/10.3970/sdhm.2017.011.191

Abstract

Tyre pressure monitoring system (TPMS) is compulsory in most countries like the United States and European Union. The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data. A difference in wheel speed would trigger an alarm based on the algorithm implemented. In this paper, machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer. The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process. The LMT (Logistic Model Tree) was used as the classifier and attained a classification accuracy of 92.5% with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features. The proposed model can be used for monitoring the automobile tyre pressure successfully.

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Keywords

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