Abstract

Vibration response analysis constitutes a pivotal approach for crack monitoring and early warning of damage identification in wind turbine blades. Traditional data-driven methods, however, demonstrate marked deficiencies in identification accuracy and generalization capability. To mitigate these issues, a method for crack damage identification in wind turbine blades is proposed, grounded in Physics-Informed Neural Networks (PINNs). Initially, utilizing a scaled-down test platform for doubly fed wind turbines, simulation experiments on blade cracks were executed. Vibration data were amassed under varying crack locations and lengths to scrutinize the intrinsic relationship between crack characteristics and the three-dimensional vibration response of the blade root bearing pedestal. Subsequently, leveraging the rotating cantilever Euler–Bernoulli beam model, the physical correlation between cracks and vibrations was dissected, and a physical information constraint model was formulated. This model was then amalgamated with a GRU-Transformer network to establish a PINN model tailored for crack damage identification. Ultimately, the model underwent testing and validation utilizing experimental data. The outcomes reveal that, in comparison to traditional data-driven models, the PINN model exhibits superior accuracy and precision in crack identification and localization, along with exceptional generalization capability and noise resilience. This research provides a novel technical pathway for enhancing the intelligence level of health monitoring for wind turbine units and holds substantial engineering significance for achieving precise condition assessment and early fault warning.

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