Ahmed A. Ogaili; Mohsin N. Hamzah; Alaa Ab. Jaber; Ehsan Ghane
Abstract
Effective structural monitoring maximizes efficiency in wind turbines, a crucial renewable energy asset. Using advanced condition monitoring techniques is crucial for reliability. This ...
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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.
