Wavelet-Based Fault Detection for Induction Machines

This document addresses the topic of fault detection in induction machines using wavelet analysis. Induction machines, commonly employed as motors and generators across various industrial applications, are susceptible to operational faults that can result in efficiency degradation and mechanical failures. Wavelet analysis serves as a powerful mathematical tool for signal processing that enables precise time-frequency localization of machine vibrations and current signatures. Through implementation of wavelet transform algorithms—such as Continuous Wavelet Transform (CWT) or Discrete Wavelet Transform (DWT)—abnormal patterns indicative of rotor bar damage, bearing wear, or stator winding faults can be extracted from sensor data. Key functions like cwt for continuous wavelet transforms or wavedec for multilevel decomposition in MATLAB facilitate feature extraction through mother wavelet selection (e.g., Daubechies, Morlet) and threshold-based denoising. This methodology supports proactive maintenance strategies by enabling early fault identification, thereby enhancing machine reliability and operational performance. Code implementations typically involve preprocessing sensor signals, applying wavelet decomposition to extract fault-related coefficients, and classifying patterns using statistical indicators like kurtosis or entropy.