The sound of a moving vehicle gives a clue of the fault. This study investigates fault detection of motorcycles using chaincode of the pseudospectrum. The motorcycle sound signals are analysed for spectral variations and these variations are traced by a chaincode. The chaincode features are used to classify the sample into healthy or faulty using dynamic time warping technique. MATLAB version 7.8.0.347 (R2009a) is used for effective implementation. The classification results obtained are over 91% and 93%, respectively, for faulty and healthy motorcycles. The results are comparable with the reported works based on wavelets. The proposed work finds applications in traffic census, traffic rule enforcement, machine fault discovery, automatic surveillance and the like.
Acoustic signal‐based approach for fault detection in motorcycles using chaincode of the pseudospectrum and dynamic time warping classifier
IET Intelligent Transport Systems ; 8 , 1 ; 21-27
2014-02-01
7 pages
Article (Journal)
Electronic Resource
English
dynamic time warping classifier , acoustic signal processing , moving vehicle sound , Matlab , traffic census , fault diagnosis , wavelet transforms , mechanical engineering computing , motorcycles , signal classification , motorcycle sound signal , automatic surveillance , acoustic signal‐based approach , fault detection , wavelet transform , chaincode feature , machine fault discovery , traffic rule enforcement , pseudospectrum chaincode , motorcycle
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