TENG, Wei ;WANG, Feng ;ZHANG, Kaili ;LIU, Yibing ;DING, Xian .
Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition.
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 60, n.1, p. 12-20, june 2018.
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/pitting-fault-detection-of-a-wind-turbine-gearbox-using-empirical-mode-decomposition/>. Date accessed: 20 jan. 2026.
doi:http://dx.doi.org/10.5545/sv-jme.2013.1295.
Teng, W., Wang, F., Zhang, K., Liu, Y., & Ding, X.
(2014).
Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition.
Strojniški vestnik - Journal of Mechanical Engineering, 60(1), 12-20.
doi:http://dx.doi.org/10.5545/sv-jme.2013.1295
@article{sv-jmesv-jme.2013.1295,
author = {Wei Teng and Feng Wang and Kaili Zhang and Yibing Liu and Xian Ding},
title = {Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {60},
number = {1},
year = {2014},
keywords = {Empirical mode decomposition; Adaptively; Fault detection; Wind turbine; Gearbox},
abstract = {The conventional method of detecting a gear fault is to demodulate the vibration signal collected from the gearbox based on the Hilbert transform; however, this requires human intervention and lacks sophistication. Empirical mode decomposition (EMD) is a significant timefrequency tool for adaptively decomposing vibration signals into a collection of intrinsic mode functions (IMFs); a fault feature can be extracted from one of IMFs to reveal the fault location and fault level of a gear or bearing in the mechanical drive system. In this paper, a multi-harmonic vibration model of a gearbox with fault modulation is presented, a conventional demodulation analysis using Hilbert transform is introduced, and the principle of EMD is illustrated. The Hilbert demodulation analysis and EMD are applied to processing field vibration signals collected from a wind turbine gearbox to detect a gear-pitting fault. The results show that EMD can extract the fault modulation information more adaptively and intelligently than Hilbert demodulation analysis can.},
issn = {0039-2480}, pages = {12-20}, doi = {10.5545/sv-jme.2013.1295},
url = {https://www.sv-jme.eu/sl/article/pitting-fault-detection-of-a-wind-turbine-gearbox-using-empirical-mode-decomposition/}
}
Teng, W.,Wang, F.,Zhang, K.,Liu, Y.,Ding, X.
2014 June 60. Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 60:1
%A Teng, Wei
%A Wang, Feng
%A Zhang, Kaili
%A Liu, Yibing
%A Ding, Xian
%D 2014
%T Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition
%B 2014
%9 Empirical mode decomposition; Adaptively; Fault detection; Wind turbine; Gearbox
%! Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition
%K Empirical mode decomposition; Adaptively; Fault detection; Wind turbine; Gearbox
%X The conventional method of detecting a gear fault is to demodulate the vibration signal collected from the gearbox based on the Hilbert transform; however, this requires human intervention and lacks sophistication. Empirical mode decomposition (EMD) is a significant timefrequency tool for adaptively decomposing vibration signals into a collection of intrinsic mode functions (IMFs); a fault feature can be extracted from one of IMFs to reveal the fault location and fault level of a gear or bearing in the mechanical drive system. In this paper, a multi-harmonic vibration model of a gearbox with fault modulation is presented, a conventional demodulation analysis using Hilbert transform is introduced, and the principle of EMD is illustrated. The Hilbert demodulation analysis and EMD are applied to processing field vibration signals collected from a wind turbine gearbox to detect a gear-pitting fault. The results show that EMD can extract the fault modulation information more adaptively and intelligently than Hilbert demodulation analysis can.
%U https://www.sv-jme.eu/sl/article/pitting-fault-detection-of-a-wind-turbine-gearbox-using-empirical-mode-decomposition/
%0 Journal Article
%R 10.5545/sv-jme.2013.1295
%& 12
%P 9
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 60
%N 1
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28
Teng, Wei, Feng Wang, Kaili Zhang, Yibing Liu, & Xian Ding.
"Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition." Strojniški vestnik - Journal of Mechanical Engineering [Online], 60.1 (2014): 12-20. Web. 20 Jan. 2026
TY - JOUR
AU - Teng, Wei
AU - Wang, Feng
AU - Zhang, Kaili
AU - Liu, Yibing
AU - Ding, Xian
PY - 2014
TI - Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition
JF - Strojniški vestnik - Journal of Mechanical Engineering
DO - 10.5545/sv-jme.2013.1295
KW - Empirical mode decomposition; Adaptively; Fault detection; Wind turbine; Gearbox
N2 - The conventional method of detecting a gear fault is to demodulate the vibration signal collected from the gearbox based on the Hilbert transform; however, this requires human intervention and lacks sophistication. Empirical mode decomposition (EMD) is a significant timefrequency tool for adaptively decomposing vibration signals into a collection of intrinsic mode functions (IMFs); a fault feature can be extracted from one of IMFs to reveal the fault location and fault level of a gear or bearing in the mechanical drive system. In this paper, a multi-harmonic vibration model of a gearbox with fault modulation is presented, a conventional demodulation analysis using Hilbert transform is introduced, and the principle of EMD is illustrated. The Hilbert demodulation analysis and EMD are applied to processing field vibration signals collected from a wind turbine gearbox to detect a gear-pitting fault. The results show that EMD can extract the fault modulation information more adaptively and intelligently than Hilbert demodulation analysis can.
UR - https://www.sv-jme.eu/sl/article/pitting-fault-detection-of-a-wind-turbine-gearbox-using-empirical-mode-decomposition/
@article{{sv-jme}{sv-jme.2013.1295},
author = {Teng, W., Wang, F., Zhang, K., Liu, Y., Ding, X.},
title = {Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {60},
number = {1},
year = {2014},
doi = {10.5545/sv-jme.2013.1295},
url = {https://www.sv-jme.eu/sl/article/pitting-fault-detection-of-a-wind-turbine-gearbox-using-empirical-mode-decomposition/}
}
TY - JOUR
AU - Teng, Wei
AU - Wang, Feng
AU - Zhang, Kaili
AU - Liu, Yibing
AU - Ding, Xian
PY - 2018/06/28
TI - Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition
JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 60, No 1 (2014): Strojniški vestnik - Journal of Mechanical Engineering
DO - 10.5545/sv-jme.2013.1295
KW - Empirical mode decomposition, Adaptively, Fault detection, Wind turbine, Gearbox
N2 - The conventional method of detecting a gear fault is to demodulate the vibration signal collected from the gearbox based on the Hilbert transform; however, this requires human intervention and lacks sophistication. Empirical mode decomposition (EMD) is a significant timefrequency tool for adaptively decomposing vibration signals into a collection of intrinsic mode functions (IMFs); a fault feature can be extracted from one of IMFs to reveal the fault location and fault level of a gear or bearing in the mechanical drive system. In this paper, a multi-harmonic vibration model of a gearbox with fault modulation is presented, a conventional demodulation analysis using Hilbert transform is introduced, and the principle of EMD is illustrated. The Hilbert demodulation analysis and EMD are applied to processing field vibration signals collected from a wind turbine gearbox to detect a gear-pitting fault. The results show that EMD can extract the fault modulation information more adaptively and intelligently than Hilbert demodulation analysis can.
UR - https://www.sv-jme.eu/sl/article/pitting-fault-detection-of-a-wind-turbine-gearbox-using-empirical-mode-decomposition/
Teng, Wei, Wang, Feng, Zhang, Kaili, Liu, Yibing, AND Ding, Xian.
"Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 60 Number 1 (28 June 2018)
