At present, there are many faults conditioning system for electrical or mechanical machines. In most of the cases, these are easily built as data for training can be collected from one machine and data for any other similar machine can be tested. For e.g. Data can be collected from one three phase induction motor for training and testing data can be from other motor, only constraint is both motors should be of same power. But in case of automobiles, data for training has to be collected from many automobiles as fault signals will vary in its characteristics with usage and distance traveled with that automobile. In recent years, automatic identification of motorbike engine faults has become a very complex and critical task. The acoustic signal produced by a motorbike engine is important information of fault diagnosis in any automobile. In this paper, an approach to identify the faults in motorbike using wavelet denoising and Support vector classification by fitting a radial based kernel is used. Preprocessing with level 4 db6 decomposition has produced optimum results. SNR for wavelets used for preprocessing is noted

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SVM radial basis function wavelet denoising