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Classifying machine learning features extracted from vibration signal with logistic model tree to monitor automobile tyre pressure
P.S. Anoop,
Published in Tech Science Press
Volume: 11
Issue: 2
Pages: 191 - 208
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. Copyright © 2017 Tech Science Press.
About the journal
JournalData powered by TypesetSDHM Structural Durability and Health Monitoring
PublisherData powered by TypesetTech Science Press