Hybrid Energy Distance and Permutation Testing for Vibration-Based Wind Turbine Blade Fault Diagnosis

Wind turbine blade failure diagnosis is of critical importance, as the inability to reliably detect such faults can result in substantial financial losses, prolonged downtime, extensive repair activities, as well as increased operation and maintenance costs. This paper introduces a new application of energy distance and permutation testing for wind turbine blade fault diagnosis based on vibration data analysis. The proposed approach combines a non-parametric energy distance and permutation testing for the identification and characterization of blade faults in wind turbines. Unlike conventional approaches, this method does not require the assumption of a normal data distribution, which is particularly important when analysing vibration signals, as such data often deviates from normality. The approach is also robust against the presence of outliers. The energy distance metric is employed to classify the blade condition of the wind turbine and to investigate subtle changes in blade behaviour by comparing healthy and corresponding faulty states while considering the entire data distribution rather than conventional summary statistics. In the second stage, a permutation test is applied to statistically validate the energy distance results via p-values. The proposed method is validated using experimental vibration datasets representing healthy and faulty blade conditions, including cracked, eroded, twisted, and imbalanced faults. These datasets are analysed under varying wind speed conditions to assess the robustness of the proposed method under different operating conditions. In the second stage, the permutation test confirms the statistical significance of the detected distributional differences, providing additional confidence in the diagnostic results. The findings indicate that the combined use of energy distance and permutation testing effectively identifies and detects the faults from the healthy blade behaviour across two different wind speed conditions. The findings of this study indicate the potential of the proposed approach as a simple, robust, and interpretable solution for wind turbine blade fault diagnosis based on vibration data analysis.