A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis

Amplitude demodulation is a key means of diagnosing bearing faults. The quality of demodulation determines the effectiveness of spectrum analysis in detecting defects. However, the quality of the demodulated signal depends on the frequency band selected for demodulation. In this paper, a new method combining the empirical wavelet transform (EWT) and operational modal analysis (OMA) is proposed. EWT acts like a filter bank in which the support boundaries of the filter are defined using OMA. The proposed method (OMA-EWT) decomposes the signal into multiple components and kurtosis values are used to select automatically the components for performing the envelope spectrum in order to extract the frequency related to the defect. The method is validated on two test benches and a comparative study is conducted with the kurtogram. The results show that the combined OMA-EWT method can improve EWT for decomposing the signal into multiple components. Using OMA-EWT, a selection of all the components excited by the defect gives more accurate diagnostic results.