A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing

By Zhike Peng, P. W. Tse, F. Chu. 2005

825 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/9401d6ab313d894d9d6bc5bfa17d8525e1f9e5be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Xingqing Wang
2. J. Y. Zhang
3. Xuefeng Chen
4. P. W. Tse
5. Ruonan Liu
6. Enrico Zio
7. F. Chu
8. Boyuan Yang

In Control and Systems Engineering

1. Xuefeng Chen
2. Xingqing Wang
3. J. Y. Zhang
4. P. W. Tse
5. Boyuan Yang
6. Ruonan Liu
7. F. Chu
8. Enrico Zio

In Mechanical Engineering

1. Xingqing Wang
2. J. Y. Zhang
3. P. W. Tse
4. Enrico Zio
5. Yong Qin
6. Huan Wang
7. Boyuan Yang
8. Xuefeng Chen

Citations in the corpus

• An improved Hilbert–Huang transform and its application in vibration signal analysis (2005, background): “The fundamental theory of the improved HHT has been presented in an authors’ paper which presents a comparison study between the improved HHT and wavelet transform in roller bearing fault diagnosis [9].”
• Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis (2020, methodology): “For the former step, fault features in time domain, frequency domain, and time–frequency domain [1] are extracted from monitoring signals by Hilbert–Huang transform [2], empirical mode decomposition [3], or local mean decomposition [4].”
• Blind Source Separation and Blind Equalization Algorithms for Mechanical Signal Separation and Identification (2006, methodology): “, 2004), Hilbert-Huang transform (Peng et al., 2005), singular value decomposition (Yang et al.”
• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, neutral): “Hilbert–Huang transform [111].”
• Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, methodology): “Time–frequency analysis such as short time Fourier transform (STFT), Hilbert-Huang transform (HHT) [24], wavelet transform [25], wavelet packet transform (WPT) and sparse decomposition [26] are often used for feature extraction.”