Transmissibility damage indicator for wind turbine blade condition monitoring

Wind turbine blade has a high failure rate as it has to constantly work under varying loads and occasionally suffer extreme weather like storm and sleet. Online condition monitoring plays an important role in finding early or minor damage to avoid catastrophic failure. Due to the large size of blade, condition monitoring systems often use multi-sensors to obtain the blade conditions in different locations. To deal with the multi-sensor data, most conventional frequency methods deal with the multiple sensor data separately without consideration of their correlations. Further, they have to use multiple indicators to represent conditions of different locations. Third, most of frequency methods are dependent on the dynamic loadings and therefore different frequency features and thresholds have to be determined under different loading conditions. To address these problems, this paper employs transmissibility analysis for multi-sensor based wind turbine blade condition monitoring. Transmissibility analysis considers the relations between different senors in the frequency domain, and only produces one single transmissibility damage indicator to represent the condition of the whole wind turbine blade. This is independent of loading conditions and computationally efficient as the main computations only involve Fast Fourier Transform (FFT). The effectiveness of the transmissibility analysis is demonstrated using both simulation example and experimental data analysis.