Feature Extraction of Fault Signals Using Wavelet Transform

This article explores how wavelet transform can be utilized for extracting features from fault signals. Wavelet transform serves as a powerful mathematical tool that converts time-domain signals into time-frequency domain representations, enabling deeper analysis of signal characteristics. When applied to fault signals, wavelet decomposition produces a set of frequency components that can be systematically organized into feature vectors. In implementation, this typically involves using wavelet functions like Daubechies or Coiflets through MATLAB's wavedec function, which performs multi-level decomposition. The resulting approximation and detail coefficients form the basis for feature construction, where statistical measures (such as energy, entropy, or standard deviation) are calculated for each decomposition level. These feature vectors subsequently serve as inputs for machine learning algorithms in fault detection and diagnosis systems, ultimately enhancing equipment reliability and performance through early anomaly identification.