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: 22 dec. 2024. 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. 22 Dec. 2024
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)
Strojniški vestnik - Journal of Mechanical Engineering 60(2014)1, 12-20
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.