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Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis by Atik Faysal; Wai Keng Ngui; M. H. Lim is a Engineering article available to read on EtoBox.
What is Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis about?
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 significa
Who reads Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Atik Faysal; Wai Keng Ngui; M. H. Lim
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Engineering (Physical Sciences)