Engineering and Technology Journal

Application of Discrete Wavelet Transform for Condition Monitoring and Fault Detection in Wind Turbine Blades: An Experimental Study

Document Type : Research Paper

Authors

1 Mechanical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

2 Physics Dept., University of Gothenburg, Gothenburg, Sweden.

Abstract

Effective structural monitoring maximizes efficiency in wind turbines, a crucial renewable energy asset. Using advanced condition monitoring techniques is crucial for reliability. This experiment shows how to use DWT and FFT for wind turbine blade fault detection. DWT allowed for multiresolution analysis of vibration signals from a healthy and eroded lab-scale turbine blade under controlled wind speeds. A 5-level DWT decomposition identified frequency sub-bands with localized fault information. The FFT post-processing of level 5 approximation coefficients revealed precise modal frequency shifts between blade states. The healthy blade showed a dominant 16 Hz mode that matched operational dynamics. Erosion caused a 24 Hz fault signature that was not present in the intact blade. Automated blade state classification was 98% accurate with 8 Hz modal separation. DWT's high sensitivity comes from nonstationary signal filtering and FFT's high-resolution spectral quantification. Comparative metric analysis confirmed DWT's superiority over FFT and statistical methods. The integrated approach combined complementary techniques to detect small defects that were previously unnoticeable. This study confirms the effectiveness of using DWT's strengths for monitoring wind turbine structural health in the future. The approach enables switching from time-based maintenance to data-driven prognostics, improving reliability by detecting failure precursors early. This study confirms DWT's effectiveness in identifying wind turbine blade faults and advancing critical techniques to prevent catastrophic failures.‎

Graphical Abstract

Highlights

  • The paper presents an experimental study utilizing DWT & FFT to detect faults in a lab-scale wind turbine blade.
  • DWT is highly advantageous for wind turbine blade monitoring .
  • A 5-level DWT decomposition was used to filter vibration data into relevant sub-bands for fault detection.
  • The healthy blade showed a 16 Hz mode matching dynamics, while the eroded blade introduced a new 24 Hz fault signature.
  •   Blade state classification was highly accurate (98%).

Keywords

Main Subjects

References
  1. REN21, Global Status Report, 2022.
  2. Ogaili, M. Hamzah, A. Jaber, Free Vibration Analysis of a Wind Turbine Blade Made of Composite Materials, International Middle Eastern Simulation and Modeling Conference 2022, MESM 2022, 2022, pp. 203–209
  3. Hameed, Y. S. Hong, Y. M. Cho, SS Ahn, and C. K. Song, Condition monitoring and fault detection of wind turbines and related algorithms: A review, Renew. Sustain. Energy Rev., 13 (2009) 1–39. https://doi.org/10.1016/j.rser.2007.05.008
  4. C. Garcia, M. A. Sanz-Bobi, and J. del Pico, SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a wind turbine gearbox, Comput. Ind., 57 (2006) 552–568. https://doi.org/10.1016/j.compind.2006.02.011
  5. Yang, R. Court, and J. Jiang, Wind turbine condition monitoring by the approach of SCADA data analysis, Renew. Energy, 53 (2013) 365–376. https://doi.org/10.1016/j.renene.2012.11.030
  6. Kim, G. Parthasarathy, O. Uluyol, and W. Foslien, Use of SCADA data for failure detection in wind turbines, Proc. ASME 2011 5th Int. Conf. Energy Sustain. collocated with ASME 2011 9th Int. Conf. Fuel Cell Sci. Eng. Technol., 2071–2079, 2011.
  7. A. Farhan, M. N. Hamzah, and A. A. Jaber, Integration of machine learning (ML) and finite element analysis (FEA) for predicting the failure modes of a small horizontal composite blade, Int. J. Renew. Energy Res., 12 (2022) 2168-2179.
  8. A. Farhan, A. A. Jaber, M. N. Hamzah, Wind turbine blades fault diagnosis based on vibration dataset analysis, Data Brief, 49 (2023) 109414. https://doi.org/10.1016/j.dib.2023.109414
  9. Chen, Z. Li, H. Guo, and J. Li, Fatigue analysis of wind turbine blade under combined loads, Procedia Eng., 10 (2011) 2807–2812.
  10. Arabian-Hoseynabadi, H. Oraee, and P. J. Tavner, Failure Modes and Effects Analysis (FMEA) for wind turbines, Int. J. Electr. Power Energy Syst., 32 (2010) 817–824. https://doi.org/10.1016/j.ijepes.2010.01.019
  11. F. Schoefs, M. Muskulus, and S. Das, Effect of erosion on wind turbine aeromechanical performance, Wind Energy, 20 (2017) 2057–2066.
  12. Wang, Y. Wang, and J. Liu, A simulation approach of annual energy loss caused by rain erosion for wind turbine blades, IOP Conf. Ser. Earth Environ. Sci., 13 (2018).
  13. Ogaili, A. A. F., Jaber, A. A., & Hamzah, M. N. (2023). Statistically Optimal Vibration Feature Selection for Fault Diagnosis in Wind Turbine Blade. Int. J. Renew. Energy Res., 13(3) 1082-1092. https://doi.org/10.20508/ijrer.v13i3
  14. Yang, P. J. Tavner, and M. R. Wilkinson, Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train, IET Renew. Power Gener., 3 (2009) 1–11. https://doi.org/10.1049/iet-rpg:20080006
  15. Hameed et al., Condition monitoring and fault detection of wind turbines and related algorithms: A review, Renew. Sustain. Energy Rev.,13 (2009) 1–39. https://doi.org/10.1016/j.rser.2007.05.008
  16. Chen, B. Zhang, and G. Vachtsevanos, Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms, IEEE Trans. Instrum. Meas., 61 (2012) 297–306. https://doi.org/10.1109/TIM.2011.2169182
  17. Kusiak and A. Verma, Monitoring wind farms with performance curves, IEEE Trans. Sustain. Energy, 4 (2013) 192–199. https://doi.org/10.1109/TSTE.2012.2212470
  18. Bartelmus and R. Zimroz, Vibration condition monitoring of planetary gearbox under varying external load, Mech. Syst. Signal Process., 23 (2009) 246–257. https://doi.org/10.1016/j.ymssp.2008.03.016
  19. Li, Y. Lei, J. Lin, and S. Luo, An improved ensemble empirical mode decomposition method for bearing fault diagnosis, Adv. Mech. Eng., 8 (2016) 168.
  20. Wang and G. Yang, Comparison of some nonstationary signal processing methods for rotating machine vibration analysis, J. Sound Vib., 290 (2006) 1229-1254.
  21. Yan and R. X. Gao, Hilbert-Huang transform-based vibration signal analysis for machine health monitoring, IEEE Trans. Instrum. Meas., 55 (2006) 2320–2329. https://doi.org/10.1109/TIM.2006.887042
  22. Wang and J. Lin, Application of wavelet analysis to gearbox vibration measurements for fault detection, J. Sound Vib., 296 (2006) 790–804.
  23. Li, M. Liang, and T. Niu, Wind turbine fault detection and isolation via motor stator current analysis, Renew. Energy, 113 (2017) 36–45.
  24. Wang, J. Lin, and M. J. Zuo, Hilbert-Huang transform and marginal spectrum analysis for detection and diagnosis of localized defects in roller bearings, J. Mech. Sci. Technol., 20 (2006) 448–459.
  25. A.F Ogaili, A.A. Jaber, M.N. Hamzah, A methodological approach for detecting multiple faults in wind turbine blades based on vibration signals and machine learning, Curved Layered Struct., 10 (2023) 20220214. https://doi.org/10.1515/cls-2022-0214
  26. Lei et al., Application of an improved kurtogram method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process., 25 (2011) 1738–1749. https://doi.org/10.1016/j.ymssp.2010.12.011
  27. Geng, J. Chen, and Z. Chen, Rolling element bearing fault detection based on time-frequency precise reserved packets, Measurement, 46 (2013) 3145–3161.
  28. Feng, M. Liang, and F. Chu, Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples, Mech. Syst. Signal Process., 38 (2013) 165–205. https://doi.org/10.1016/j.ymssp.2013.01.017
  29. Yang, R. Court, and J. Jiang, "Wind turbine condition monitoring by the approach of SCADA data analysis, Renew. Energy, 53 (2013) 365–376. https://doi.org/10.1016/j.renene.2012.11.030
  30. Subramanian, M. S. D. Kumar, and B. Chakraborty, Wavelet–PCA-based coating fault detection for OWT blades from SCADA data, IEEE Sens. J., 15 (2015) 5605–5611.
  31. Li, M. Liang, and Z. Xu, Fault detection of wind turbines based on power curve using long-window SCADA data, Renew. Energy, 126 (2018) 822–836.
  32. Lu and Y. Li, A comparative study of continuous wavelet transform, Hilbert Huang transform and Fourier transform in fault diagnosis of rotating machines, Proc. 8th IEEE Int. Conf. Computational Intelligence and Security, Guangzhou, 2012, pp. 268-272.
  33. Du, Y.; Zhou, S.; Jing, X.; Peng, Y.; Wu, H.; Kwok, N., Damage detection techniques for wind turbine blades: A review. Mech. Syst. Signal Process. 141 (2019) 106445. http://dx.doi.org/10.1016/j.ymssp.2019.106445
  34. Dao, C.; Kazemtabrizi, B.; Crabtree, C., Wind turbine reliability data review and impacts on leve lised cost of energy. Wind Energy, 22 ( 2019) 1848–1871. https://doi.org/10.1002/we.2404
  35. Wang W, Xue Y, He C, Zhao Y., Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades. Energies. 15 (2022) 5672. https://doi.org/10.3390/en15155672
  36. Katsaprakakis DA, Papadakis N, Ntintakis I., A Comprehensive Analysis of Wind Turbine Blade Damage. Energies. 14 (2021) 5974. https://doi.org/10.3390/en14185974
  37. Reddy, Abhishek, V. Indragandhi, Logesh Ravi, and V. Subramaniyaswamy. Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics. Measurement 147 (2019) 106823. https://doi.org/10.1016/j.measurement.2019.07.051
  38. Xu, Jin, Xian Ding, Yongli Gong, Ning Wu, and Huihuang Yan. Rotor imbalance detection and quantification in wind turbines via vibration analysis. Wind Eng., 46 (2022) 3-11. http://dx.doi.org/10.1177/0309524X21999841
  39. Furht, Borko. Discrete wavelet transform (DWT). Encyclopedia of Multimedia, Springer, USA (2008).
  40. Bakir, T., Boussaid, B., Hamdaoui, R., Abdelkrim, M.N. and Aubrun, C., 2015, March. Fault detection in wind turbine system using wavelet transform: Multiresolution analysis. In 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15) 1-6. IEEE. https://doi.org/10.1109/SSD.2015.7348223
  41. Rhif M, Ben Abbes A, Farah IR, Martínez B, Sang Y.. Wavelet Transform Application for/in Nonstationary Time-Series Analysis: A Review. Appl. Sci., 9 (2019) 1345. https://doi.org/10.3390/app9071345
  42. Abdulraheem, Khalid Fatihi, and Ghassan Al-Kindi., Wind Turbine Blade Fault Detection Using Wavelet Power Spectrum and Experimental Modal Analysis. Int. J. Renew. Energy Res., 8 (2018) 2167-2179.
  43. Guo, Y., Yan, W. and Bao, Z., 2010, July. Gear fault diagnosis of wind turbine based on discrete wavelet transform. In 2010 8th World Congress on Intelligent Control and Automation, 5804-5808, IEEE.
  44. Liu, Z. Wang, and X. Liu, Wind turbine fault detection and isolation using deep neural networks with SCADA data, IEEE Trans. Ind. Electron., 67 (2020) 7556–7566.
  45. Jaber, A.A.; Bicker, R., The optimum selection of wavelet transform parameters for the purpose of fault detection in an industrial robot. In Proceedings of the 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE2014), Penang, Malaysia, 28–30 November 2014; pp. 304–309.
  46. Peng and F. Chu, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Syst. Signal Process., 18 (2004) 199–221. https://doi.org/10.1016/S0888-3270(03)00075-X
  47. Ghane, H. Nejad, F. Taheri-Sani, and M. Mohtasebi, Vibration-based damage detection in wind turbine blades using Phase-based Motion magnification and Convolutional Neural Networks, Appl. Acoust., 159 (2020) 107091.
  48. Yang and J. Jiang, Wind turbine condition monitoring and reliability analysis by SCADA information, in PHM 2012-Progn. Heal. Manag. Conf., Oct. (2012) 1–7. https://doi.org/10.1109/MACE.2011.5987329
  49. Li, M.-Y. Chow, Y. Tipsuwanporn, and J. C. Hung, Neural-network-based motor rolling bearing fault diagnosis, IEEE Trans. Ind. Electron., 47 (2000) 1060–1069. https://doi.org/10.1109/41.873214
  50. Dalpiaz et al., Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears, Mech. Syst. Signal Process., 14 (2000) 387–412. https://doi.org/10.1006/mssp.1999.1294
  51. Wang and P. D. McFadden, Application of wavelets to gearbox vibration signals for fault detection, J. Sound Vib., 192 (1996) 927–939. https://doi.org/10.1006/jsvi.1996.0226
  52. Lu, Y. Li, Z. Wu, and Z. Yang, A review of recent advances in wind turbine condition monitoring and fault diagnosis, in IEEE Power Electron. Mach. Wind Appl. PEMWA 2009, Jun. 2009, pp. 1–7. https://doi.org/10.1109/PEMWA.2009.5208325
Engineering and Technology Journal
Volume 42, Issue 1
Mechanical Engineering
17 articles
January 2024
Page 104-116
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