Acoustic Pseudospectrum Based Fault Localization Inmotorcycles

Vehicles generate dissimilar sound patterns under different health conditions. The sound generated by the vehicles gives a clue of some of the faults. Automotive experts diagnose the faults in vehicles based on the produced sound. This paper presents a methodology for fault source localization in motorcycles using the estimated pseudospectra of the sound signals. The pseudospectra are traced to generate a chaincode. The generated chaincode is transformed into an eigenvector, which is used as the feature vector for classification using artificial neural network (ANN). The overall classification accuracy is 88%. The proposed work finds applications in traffic census of the vehicles, traffic rule observance, machine fault detection and localization, automatic acoustic surveillance and the like.