Bearing Fault Diagnosis Using Wavelet Transform Methodology

We can develop MATLAB programs employing wavelet transform methods to conduct bearing fault diagnosis. Wavelet transform serves as a powerful signal analysis technique that enables detection and identification of potential bearing faults. Through wavelet decomposition of vibration signals, we can extract characteristic fault frequencies that precisely indicate bearing health conditions. The implementation typically involves using MATLAB's Wavelet Toolbox functions like wavedec for multilevel decomposition and waverec for reconstruction. During fault diagnosis, various wavelet basis functions (such as Daubechies, Symlets, or Coiflets) can be tested to optimize analysis results. The algorithm workflow generally includes: signal acquisition, wavelet coefficient computation, feature extraction through detail coefficients analysis, and fault characteristic frequency identification. By programming in MATLAB with wavelet transform methodologies, we can effectively diagnose bearing conditions and predict potential failures through pattern recognition in transformed signals.