TWO-STAGE RELIABILITY ASSESSMENT OF WIND TURBINE BLADES CONSIDERING RANDOM FAILURE THRESHOLDS
To address the limitations of existing two-stage degradation reliability assessment methods for wind turbine blades,specifically the neglect of failure threshold randomness and the accuracy of changepoint detection in the degradation process,a novel approach is proposed.This method is based on a nonlinear Wiener degradation process and considers the impact of random failure thresholds across different stages.The corrected akaike information criterion(AICc)is introduced to determine the optimal changepoint location,followed by interval estimation.Maximum likelihood estimation is employed to determine the drift and diffusion coefficients for the two stages based on the identified changepoint.Subsequently,a reliability model for blade degradation is established using data obtained from fatigue crack propagation simulation experiments.To validate the accuracy of the model predictions,an empirical analysis is conducted using updated wind turbine blade data via an adaptive algorithm.The results demonstrate that accounting for failure threshold randomness and changepoint accuracy significantly impacts degradation modeling,thereby enhancing the precision of reliability assessments.
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