International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.5, No.1, Jan. 2013

Acoustic Signal Based Fault Detection in Motorcycles – A Comparative Study of Classifiers

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Basavaraj S. Anami,Veerappa B. Pagi

Index Terms

Acoustic fault detection, ANN classifier, Support vector machines, K-nearest neighbour classifier, DTW classifier


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.

Cite This Paper

Basavaraj S. Anami,Veerappa B. Pagi,"Acoustic Signal Based Fault Detection in Motorcycles – A Comparative Study of Classifiers", IJIGSP, vol.5, no.1, pp.8-15, 2013.DOI: 10.5815/ijigsp.2013.01.02



[2]C. Kwak and O. Kwon, Cardiac Disorder Classification Based on Extreme Learning Machine, World Academy of Science, Engineering and Technology, 2008, 48, 435-438.

[3]C. H. Lee, C. C. Han, C. C. Chuang, Automatic Classification of Bird Species From Their Sounds Using Two-Dimensional Cepstral Coefficients, IEEE Trans. Audio, Speech, And Language Processing, 2008, 16(8), 1541-1550.

[4]A. Averbuch, V. Zheludev, N. Rabin and A. Schclar, Wavelet Based Acoustic Detection of Moving Vehicles, J. Multidimensional Systems and Signal Processing, 2009, 20(1) 55-80.

[5]B. S. Anami and V. B. Pagi, An Acoustic Signature Based Neural Network Model for Type Recognition of Two-Wheelers, Proc. of the IEEE Int. Conf. on Multimedia Systems, Signal Processing and Communication Technologies, March 14-16; Aligarh, India, 2009, 28-31.

[6]B. S. Anami, V. B. Pagi, and S. M. Magi, Wavelet Based Acoustic Analysis for Determining Health Condition of Two-Wheelers, Elsevier J. Applied Acoustics, 2011, 72(7), 464-469.

[7]J. D.Wu, E. C. Chang, S. Y. Liao, J. M. Kuo and C. K. Huang, Fault Classification of a Faulty Engine Platform using Wavelet Transform and Artificial Neural Network, Proc. of the Int. MultiConf. of Engineers and Computer Scientists, March 18-20; HongKong, 2009, 1, 58-63.

[8]A. Datta, C. Mavroidis, J. Krishnasamy and M. Hosek, Neural Netowrk Based Fault Diagnostics Of Industrial Robots Using Wavelet Multi-Resolution Analysis, Proc. of the American Control Conference, July 9-13; New York, 2007, 1858-1863.

[9]J. Cheng, D. Yu, and Y. Yang, A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM, EURASIP J. Advances in Signal Processing, doi: 10.1155/2008/647135, 2008.

[10]Y. Ganjdanesh, Y. S. Manjili, M. Vafaei, E. Zamanizadeh, E. Jahanshahii, Fuzzy Fault Detection and Diagnosis under Severely Noisy Conditions using Feature-based Approaches, American Control Conf., June 11-13; Westin Seattle Hotel, Seattle, Washington, 2008, 3319-3324.

[11]K. Mehrotra, C. K. Mohan and S. Ranka, Elements of Artificial Neural Networks, The MIT Press, 1996.

[12]Xu Shuxiang and Ling Chen, A Novel Approach for Determining the Optimal Number of Hidden Layer Neurons for FNN’s and Its Application in Data Mining, Proc. of the 5th International Conference on Information Technology and Applications, June 23–26; Cairns, Queensland, 2008, 683-686.

[13]H. Sakoe, and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans. on Acoustics, Speech and Signal Processing, 1978, 26(1), 43-49.