Acoustic Signal Based Fault Detection in Motorcycles – A Comparative Study of Classifiers
Автор: Basavaraj S. Anami, Veerappa B. Pagi
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 1 vol.5, 2013 года.
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The sound patterns generated by the vehicles give a clue of the health conditions. The paper presents the fault detection of motorcycles based on the acoustic signals. Simple temporal and spectral features are used as input to four types of classifiers, namely, dynamic time warping (DTW), artificial neural network (ANN), k-nearest neighbor (k-NN) and support vector machine (SVM), for a suitability study in automatic fault detection. Amongst these classifiers the k-NN is found to be simple and suitable for this work. The overall classification accuracy exhibited by k-NN classifier is over 90%. The work finds applications in automatic surveillance, detection of non-compliance with traffic rules, identification of unlawful mixture of fuel, detection of over-aged vehicles on road, vehicle fault diagnosis and the like.
Acoustic fault detection, ANN classifier, Support vector machines, K-nearest neighbour classifier, DTW classifier
Короткий адрес: https://sciup.org/15012518
IDR: 15012518
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