Service station experts examine the sound patterns of the motorcycles to diagnose the faults. Automatic fault diagnosis is a challenging task and more so is recognition of multiple faults. This study presents a methodology for localisation of multiple faults in motorcycles. The sound signatures of multiple faults are constructed by fusing the individual signatures of faults from engine and exhaust subsystems. Energy distributions in the approximation coefficients of wavelet packets are used as features. Among the classifiers used, artificial neural network is found suitable for detecting the presence of multiple faults. The recognition accuracy is over 78% when trained with individual fault signatures and over 88% when trained with combined fault signatures.
Localisation of multiple faults in motorcycles based on the wavelet packet analysis of the produced sounds
IET Intelligent Transport Systems ; 7 , 3 ; 296-304
01.09.2013
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
wavelet transforms , motorcycles , automotive engineering , sound signature , approximation coefficient , multifault localisation , engine , automatic fault diagnosis , artificial neural network , fault diagnosis , exhaust systems , energy distribution , neural nets , wavelet packet analysis , motorcycle , sound patterns , fault signature , engines , approximation theory , fault recognition , mechanical engineering computing , exhaust subsystem
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