A Novel Rolling Bearing Fault Diagnosis Method Based on Empirical Wavelet Transform and Spectral Trend

Empirical wavelet transform (EWT) is a new adaptive signal decomposition method based on wavelet theory, the main idea is to establish an appropriate set of empirical wavelet filter banks for adaptive signal decomposition. EWT has been demonstrated its effectiveness in some applications. However, the unreasonable spectrum segmentation will lead to the emergence of many invalid components. In this paper, a novel spectral segmentation method is proposed to improve the drawback of EWT in boundary division. The proposed method takes into account the waveform of the spectrum itself. First, different spectral trends are obtained by iteratively calculating the mean of the upper envelope function and the lower envelope function of the spectrum. Then, the most appropriate one is got according to the criterion and the spectrum segmentation is achieved by detecting the local minimum of the trend. Finally, empirical modes are obtained by a set of bandpass filters. The effectiveness and efficiency of the method are verified by two simulation signals. Finally, the proposed method is applied to fault diagnosis of the inner and outer race of rolling bearings, respectively. The results indicate that the method can accurately and effectively identify fault information.