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
01.06.2014
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
statistical analysis , fault diagnosis , mechanical engineering computing , spectral analysis , machinery fault diagnosis , acoustic signal detection , valves , sound patterns , spectral behaviour , vehicle fault diagnosis , exhaust systems , acoustic signal based motorcycle fault detection , musical instrument fault diagnosis , dynamic time warping classifier , timing chain faults , sound signal pseudospectrum chaincode , valve‐setting , motorcycles , signal classification , wavelet subbands , feature extraction , working conditions , acoustic signal based motorcycle fault localisation , muffler leakage , silencers , statistical features , fault detection stage
Acoustic signal based detection and localisation of faults in motorcycles
IET | 2014
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