Gear fault detection, diagnosis, and classification are critical yet challenging
tasks due to the similarity in spectral features among various gear fault types.
This study presents an automated approach for detecting, identifying, and
classifying gear defects by leveraging Wavelet Packet Transform (WPT) for
signal decomposition and feature extraction.
