Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition

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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/article/pitting-fault-detection-of-a-wind-turbine-gearbox-using-empirical-mode-decomposition/>. Date accessed: 11 may. 2021. 
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/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/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.  11 May. 2021
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/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/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/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)

Authors

Affiliations

  • North China Electric Power University, School of Energy Power and Mechanical Engineering, China 1

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 60(2014)1, 12-20

https://doi.org/10.5545/sv-jme.2013.1295

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.

Empirical mode decomposition; Adaptively; Fault detection; Wind turbine; Gearbox