Fault Feature Vector Extraction and Normalization Using Wavelet Packets

In this research paper, the authors employ wavelet packet analysis to extract fault feature vectors followed by normalization processing. This methodology demonstrates significant practical value, and the authors have included relevant program code fragments within the paper. Through the utilization of these programs, readers can gain deeper insights into and practical application of the research outcomes. The implementation typically involves several key computational steps: First, wavelet packet decomposition is performed on the input signal using functions like wpdec() to obtain energy distribution across different frequency bands. Then, feature vectors are constructed by calculating energy values from specific wavelet packet nodes. Finally, normalization is applied using methods such as min-max scaling or z-score standardization to ensure consistent feature ranges. The practical nature of this approach, combined with the provided code implementation, enables readers to better comprehend and apply the methodology, thereby achieving improved results in fault feature extraction applications. The code likely includes functions for signal preprocessing, wavelet packet tree generation, feature calculation, and normalization routines that facilitate reproducible research outcomes.