Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform

By V. Rai, A. Mohanty. 2007

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Early detection of tooth crack damage in gearbox using empirical wavelet transform combined by Hilbert transform (2017, methodology): “Traditional signal processing techniques, such as Fast Fourier Transform (FFT) can convert the signal into the frequency domain, but it cannot provide both time and frequency information simultaneously (Rai and Mohanty, 2007; Mark et al., 2010).”
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017, methodology): “Common signal processing techniques used to extract the representative features from the raw signal include time-domain statistical analysis [6], wavelet transformation [7], and Fourier spectral analysis [8].”
A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, methodology): “Rai and Mohanty (2007) proposed a bearing fault diagnosis method based on fast fourier transformation of intrinsic mode functions from Hilbert–Huang transform process.”
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “The methods like fast Fourier transform (FFT) [11], wavelet transformation (WT) [12], empirical mode decomposition (EMD) [13], ensemble empirical mode decomposition (EEMD) [14], empirical wavelet transform (EWT), wavelet packet transform (WPT) [15], variational mode decomposition (VMD), stochastic…”