Bayesian approach of elliptical loci and RAPID for damage localization in wind turbine blade

Abstract

This investigation addresses the issue of damage detection and localization in wind turbine blade laminates. This paper proposes a novel approach that integrates the elliptical trajectory and probabilistic imaging method using the Bayesian framework. This method employs multiple damage-sensitive features to enhance the reliability and robustness of sensor arrays. The algorithm is optimized by analyzing the propagation characteristics of Lamb waves in composite blade laminates. A numerical simulation is conducted on a 1.5MW wind turbine blade laminate model, incorporating the scattered wave signal, wave arrival time, and correlation coefficient as damage characteristic signals. Markov Chain Monte Carlo (MCMC) sampling method is adopted to obtain the posterior distribution of the damage location and achieve accurate localization of blade delamination damage. The experimental results indicate that the damage localization algorithm, which utilizes the Bayesian approach, achieves an accuracy of approximately 97.04% in localizing delamination damage in blade laminates.