Acoustic signal based detection and localisation of faults in motorcycles. Issue 4 (1st June 2014)

• Record Type: Journal Article
• Authors: Anami, Basavaraj S.; Pagi, Veerappa B.
• Is Part Of: IET intelligent transport systems. Volume 8:Issue 4(2014)
• Page Start: 345
• Page End: 351
• Publication Date: 2014-06-01
• Subjects: acoustic signal detection -- exhaust systems -- fault diagnosis -- feature extraction -- mechanical engineering computing -- motorcycles -- signal classification -- silencers -- spectral analysis -- statistical analysis -- valves; acoustic signal based motorcycle fault detection -- acoustic signal based motorcycle fault localisation -- sound patterns -- working conditions -- spectral behaviour -- fault detection stage -- sound signal pseudospectrum chaincode -- statistical features -- wavelet subbands -- dynamic time warping classifier -- valve‐setting -- muffler leakage -- timing chain faults -- vehicle fault diagnosis -- machinery fault diagnosis -- musical instrument fault diagnosis; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2012.0193
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.