Vehicles produce dissimilar sound patterns under different working conditions. The study approaches detection and localisation of faults in motorcycles, by exploiting the variations in the spectral behaviour. Fault detection stage uses chaincode of the pseudospectrum of the sound signal. Fault localisation stage uses statistical features derived from the wavelet subbands. Dynamic time warping classifier is used for classification of samples into healthy and faulty in the first stage. In essence, the same classifier classifies the faulty samples into valve-setting, muffler leakage and timing chain faults in the second stage. Classification results are over 90% for both the stages. The proposed study finds applications in surveillance, fault diagnosis of vehicles, machinery, musical instruments etc.
Acoustic signal based detection and localisation of faults in motorcycles
IET Intelligent Transport Systems ; 8 , 4 ; 345-351
2014-06-01
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
motorcycles , valve-setting , sound signal pseudospectrum chaincode , working conditions , muffler leakage , spectral analysis , statistical features , fault detection stage , silencers , timing chain faults , feature extraction , mechanical engineering computing , acoustic signal based motorcycle fault localisation , musical instrument fault diagnosis , machinery fault diagnosis , valves , statistical analysis , wavelet subbands , fault diagnosis , exhaust systems , acoustic signal detection , acoustic signal based motorcycle fault detection , dynamic time warping classifier , signal classification , sound patterns , vehicle fault diagnosis , spectral behaviour
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Acoustic signal based detection and localisation of faults in motorcycles
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