Feature extraction from wavelet coefficients for pattern recognition tasks

This paper deals with the assessment of the value of process parameters from the wavelet coefficients of a measured process signal. Since a direct assessment from all wavelet coefficients will often turn out to be tedious or leads to inaccurate results, a preprocessing routine that computes robust features directly correlated to the process parameters is highly desirable. In this paper, a new efficient feature extraction method based on the fast wavelet transform is presented. This method divides the matrix of computed wavelet coefficients into clusters equal to rowvectors. The important frequency ranges have a larger number of clusters than the less important frequency ranges. The features of a process signal are provided by the euclidean norms of each such vector. The effectiveness of this new method has been verified on a flank wear estimation problem in turning processes.