Feature Extraction of Rubbing Fault Signals Using Wavelet Transform

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Resource Overview

Feature extraction of rubbing fault signals based on wavelet transform enables visualization of original signals, shaft centerline orbits, frequency spectra, and reconstructed signals from multi-level wavelet decomposition with MATLAB implementation approaches

Detailed Documentation

In industrial equipment maintenance, feature extraction of rubbing fault signals using wavelet transform is critical for condition monitoring. By extracting various signal characteristics through MATLAB's wavelet toolbox functions (e.g., wavedec for decomposition and waverec for reconstruction), we can better understand equipment operating conditions for timely maintenance. The process involves generating visualizations including: original signal plots using plot() function, shaft centerline orbits through xy-coordinate mapping, frequency spectra via FFT implementation (fft()), and multi-level wavelet reconstructed signals using wrcoef(). Different wavelet functions (dbN, symN) and decomposition levels can be optimized through wfilters() and wmaxlev() functions to obtain more accurate diagnostic information. These visualizations and parameters are essential for analyzing equipment health states and implementing predictive maintenance strategies.

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