Adaptive Reinforced Empirical Morlet Wavelet Transform and Its Application in Fault Diagnosis of Rotating Machinery

Identifying impact fault features from fault vibration signal is significantly meaningful for the fault diagnosis and condition monitoring of rotating machinery. Given defects and the working conditions, impact features are covered by background noise. A new method named empirical wavelet transform (EWT) has been receiving attention from the researchers and engineers. However, detecting boundaries by using the local maxima method from Fourier spectra and capturing the impact features through Meyer wavelet are the two crucial drawbacks of EWT. The former might be invalidated by the influence of non-stationary and noise frequency, and the latter is inappropriate for impact signal features. Therefore, reinforced empirical Morlet wavelet transform (REMWT) is proposed to overcome these shortcomings and efficiently diagnose fault features. In this method, the frequency spectrum boundaries are adaptively detected from the inner product of spectral kurtosis and Gaussian function via scale space representation, which can enhance the frequency character of impact features in vibration signals. Then, the constructed empirical Morlet wavelet serves as the adaptive filter bank for decomposing the signal into several empirical modes on the basis of spectrum boundaries. The meaningful component is selected via the maximum Pearson correlation coefficient method, and the envelope spectrum is used to accurately extract the fault features. The proposed method is then used to diagnose the fault features from the collected vibration signals. The results show its effectiveness and outstanding performance.