Wavelet-based acoustic analysis for determining health condition of motorized two-wheelers

The garage mechanics assess the health condition of a vehicle based on sounds of vehicles. This expert knowledge needs to be stored in repositories and employed for automatic fault diagnosis. In this work, we propose a method to determine the health condition of motorcycles, based on discrete wavelet transform (DWT). The 1D central contour moments and invariant contour moments, of approximation coefficients of DWT form the feature vector. Dynamic time warping (DTW) classifier along with Euclidean distance measure is used for health classification. The results show over 98% classification accuracy for some subbands and some feature sets.