Airborne Acoustic and Empirical Mode Decomposition Techniques for Wind Turbine Condition Monitoring
DOI:
https://doi.org/10.59743/jbs.v38i1.323Keywords:
Empirical Mode Decomposition, Condenser Microphones, Wind Turbine Air-borne Acoustic Signals, Rotor imbalanceAbstract
Air-borne acoustic signals offer a number of advantages over other monitoring techniques, primarily because they can be measured at a distance away from that machine and contain very helpful information of that machine condition. However, they are prone to background noise, which makes extracting information more difficult, Microphones offer inexpensive and remote media for measuring wind turbine air-borne acoustic signals. Therefore, the proposed condition monitoring technique is based on airborne-acoustic signals to monitor wind turbine blades condition and introduces a non-contact method for assessing the health of wind turbine blades. The approach involves analyzing remotely captured airborne acoustic signals by decomposing them into their intrinsic components using the empirical mode decomposition (EMD) technique. The presence of wind turbine rotor imbalance is evaluated by monitoring the amplitude of a frequency component corresponding to rotating speed, A condenser microphone located 50 cm away from the test rig was used to record the emitted acoustic signals. The mechanical faults were simulated for a rotor imbalance on the test rig and the tests were carried out at various rotation speeds. Fundamental characteristics of the measured signals were extracted for the healthy rotor and the condition where two of the three blades were healthy and one blade was replaced with a blade that was 10 %, 20 % and 30% heavier than the healthy one. The results show that the EMD/airborne acoustic signal technique is very sensitive to blade unbalance faults and found to increase in a linear fashion with the severity of the fault.
Downloads
References
N. Baydar, "THE VIBRO-ACOUSTIC MONITORING OF GEARBOXES, in Department of Mechanical Engineering. University of Manchester Manchester," 2000.
Z. Xu et al., "A state-of-the-art review of the vibration and noise of wind turbine drivetrains," Sustainable Energy Technologies and Assessments, vol. 48, p. 101629, 2021/12/01/ 2021.
F. Qu, J. Liu, H. Zhu, and B. Zhou, "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, vol. 262, p. 114469, 2020/03/15/ 2020.
J. Yan, L. Yongqian, L. Li, and X. Ren, "Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm," Sensors, vol. 23, p. 5873, 06/25 2023.
A. Ogaili, Hamzah, M., Jaber, A., & Ghane, E., "Application of Discrete Wavelet Transform for Condition Monitoring and Fault Detection in Wind Turbine Blades: An Experimental Study," Unviversity of Technology- Iraq, 2024.
W. Y. Zhiqiang, Xu Yuanying, Mei., " Damage Diagnosis for Wind Turbine Blades Based on the Shifting Distance of Characteristic Frequency. in Image and Signal Processing. ," CISP '09. 2nd International Congress on, 2009.
A. Abouhnik, G. Ibrahim, R. Shnibha, and A. Albarbar, Wind Turbine Blade Fault Detection Using the Empirical Mode Decomposition Method; Numerical Simulation and Experimental Testing. 2011.
G. K. a. S. a. A. S. A. K. Singh, "Induction machine drive condition monitoring and diagnostic research a survey.," Electric Power Systems Research, vol. 64(2): p. 145-158., 2003.
M. Lang, et al., , "Noise reduction using an undecimated discrete wavelet transform," Signal Processing Letters, IEEE,, vol. . 3(1): p. 10-12., 1996.
G. R. I. A. Abouhnik, A. Albarbar and Mohammed sh-eldin "wind turbine blades fault detection based on principal component analysis " in International conference on applications and design in mechanical engineering (icadme 2012), Perlis, Malaysia., 27-28, February 2012.
B. M. Fazenda, "Acoustic based condition monitoring of turbine blades," presented at the 18th International Congress on Sound and Vibration, Rio de Janeiro, Brazil, 2011.
A. Albarbar, F. Gu, A. Ball, and A. Starr, "Acoustic monitoring of engine fuel injection based on adaptive filtering techniques," Applied Acoustics, vol. 71, pp. 1132-1141, 12/01 2010.
A. Abouhnik et al., "The Extraction of Wind Turbine Condition Related Features Using Air-Borne Acoustic Signals," in Proceedings of TEPEN 2022, Cham, H. Zhang, Y. Ji, T. Liu, X. Sun, and A. D. Ball, Eds., 2023// 2023: Springer Nature Switzerland, pp. 124-137.
Anthony L. Rogers James F. Manwell, "Wind Turbine Noise Issues," Renewable Energy Research Laboratory, Center for Energy Efficiency and Renewable Energy. Department of Mechanical and Industrial Engineering. University of Massachusetts at Amherst, 2004.
S. Wagner, Bareiss, R. and Guidati, G. and, " Wind Turbine Noise. ," Springer. New York., 1996.
Peeters, Simulation of dynamic drive train loads in a wind turbine. ISBN 90-5682-728-6, Katholieke Universiteit Leuven, : Faculteit Ingenieurswetenschappen Arenbergkasteel-B-3001 Heverlee (Belgium), 2006.
L. G. B.O. Al-Bedoor, S.A. Adewusi, Y. Al-Nassar, M. Abdlsamad "Experiments on the extraction of blade vibration signature from the shaft torsional vibration signals," Journal of Quality in Maintenance Engineering, vol. Vol. 9 Iss: 2, pp. pp.144 - 159, 2003.
N. Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903-995, 03/08 1998.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Basic Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
