Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis

Purpose Although noise-assisted decomposition methods, ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) can reduce the drawbacks of empirical mode decomposition (EMD), they cannot fully eliminate the presence of white noise. Moreover, they require large number of ensembles for a desired output which takes much computational time Methods In this paper, an improved method named noise eliminated EEMD (NEEEMD) was proposed to reduce further the white noise in the intrinsic functions and keep the ensembles optimum. The NEEEMD algorithm also decomposes the ensemble of white noise signals using EMD and subtracts from the outputs of EEMD. A simulated signal was used to demonstrate the performance of NEEEMD using root-mean-square error (RRMSE) and time and envelope spectrum kurtosis (TESK). A sensitive mode (SM) selection method was proposed to select the most sensitive intrinsic mode functions (IMFs) from NEEEMD which takes multiplication of kurtosis in the time domain and energy-entropy in the frequency domain. Finally, to enhance the signal's fault-related impulses, an advanced filter called MOMEDA was applied to the most sensitive IMF. Results The significance of the proposed method was illustrated using the envelope spectrum from bearing signals containing different types of faults at various speeds and motor loads. The output of the proposed method, EEMD and CEEMD was compared using the envelope spectrum to identify fault characteristic impulses. Envelope spectrum analysis proved that our proposed method performed better in every case by providing more fault-related impulses. Conclusion The proposed method can be applied as a more accurate fault diagnosis system for rotor bearings as it can identify more fault characteristic impulses from the envelope spectrum.