Empirical Wavelet Transform and Power Spectral Entropy for Rotating Machinery Fault Diagnosis

摘要

In order to extract the fault feature of rotating machines, a new method based on the empirical wavelet transform (EWT) and power spectral entropy (PSE) is proposed. EWT is introduced to first decompose the raw signal into several intrinsic mode function (IMF) signals. The power spectral entropy is used to quantify the complexity and uncertainty of each constructed component's spectra; the difference value (D-value) between the neighboring entropies is, therefore, calculated to indicate the most information of reconstructed signals. Finally, the real signal is tested by the proposed method, whose results show that it can effectively extract the most abundant fault characteristic information in machinery fault signals.