EMD is one of the most powerful signal processing techniques, particularly in nonlinear and non-stationary signal processing. At present, EMD and Hilbert-Huang transform (HHT) have been widely used in fault diagnosis of rotating machineries. Lei et al. [
189] surveyed and summarized the recent research, development, and application of EMD in terms of key components, such as rolling element bearings, gears, and rotors. Babu et al. [
190] applied HHT to detect the transverse breathing crack from time response of the cracked rotor passing to its critical speed. Lin and Chu [
191] applied HHT on AE feature extraction of natural fatigue cracks induced on rotating shafts, and demonstrated that HHT is a better tool for conducting natural fatigue crack characterization compared with fast Fourier transform (FFT) and continuous WT (CWT). A past study investigated the start-up transient response of a rotor with a propagating transverse crack via EMD; the authors extracted the one-, two-, and three-time rotating frequency vibration components during the start-up process [
192]. Given that HHT has the capability of processing nonlinear vibration signals, Zhang and Yan [
193] proposed an HHT-based signal processing method to obtain the natural frequency of the multi-cracks cantilever beam with a higher resolution. In Ref. [
194], three signal processing tools, namely, STFT, CWT, and HHT, are compared to evaluate their detection performance and computational time in a rotor bearing system. Xu [
195] proposed a methodology based on translation-invariant denoising and HHT to detect rolling element bearing faults against strong background noise. Li and Wang [
196] summarized the development and application of HHT for solving the problem of rolling bearing fault diagnosis from several aspects. Lei et al. [
197] introduced the enhanced empirical mode decomposition (EEMD) for fault diagnosis of rotating machineries, in which the problem of the mixing modes is partially solved by adding white noise to the original signal. Feng et al. [
198] proposed a new method based on EEMD and the Teager energy operator to extract the characteristic frequency of bearing fault, which demonstrated better performance than the traditional spectral analysis and the squared envelope spectral analysis methods. Meanwhile, Ricci and Pennacchi [
91] introduced a merit index for the automatic selection of the intrinsic mode functions used to obtain the HHT spectrum, which they verified by using a spiral bevel gearbox with high contact ratio. Wu et al. [
199] utilized the instantaneous dimensionless frequency normalization and HHT to characterize the different gear faults, including worn tooth, broken tooth, and gear unbalance, under variable rotating speed levels. Furthermore, the support vector machine (SVM) has been used to classify the different gear faults.