Wavelet Packet Decomposition for Bearing Fault Signals
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This process involves performing wavelet packet decomposition on bearing fault signals, reconstructing the frequency band signal with maximum energy, and calculating sample entropy for the reconstructed signal. In implementation, the wavelet packet decomposition algorithm can be applied using functions like wpdec in MATLAB to decompose bearing fault signals into sub-signals across different frequency bands. The reconstruction of the maximum energy frequency band signal, achievable through wprcoef function, helps extract dominant characteristic information. Finally, sample entropy calculation, which can be implemented using approximate entropy algorithms with parameters like embedding dimension and tolerance threshold, evaluates the signal's complexity and irregularity. Through these analytical steps, we gain comprehensive insights into bearing fault signal characteristics, providing more accurate information for fault diagnosis and prediction.
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