Open AccessAcoustic signal based detection and localisation of faults in motorcycles
Author(s) -
Anami Basavaraj S.,
Pagi Veerappa B.
Publication year - 2014
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2012.0193
Subject(s) - classifier (uml) , fault detection and isolation , dynamic time warping , wavelet , pattern recognition (psychology) , computer science , engineering , artificial intelligence , fault (geology) , real time computing , speech recognition , actuator , seismology , geology
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.