Dual Domain Fault Diagnosis of Wind Turbine Gearbox Based on Physical Information Neural Network

As an important component of clean energy, wind power generation is of great significance in promoting energy structure transformation and achieving sustainable development. It is particularly crucial to develop precise and effective fault diagnosis technology to ensure the safe and stable operation of wind turbines. This paper proposes a fault diagnosis method for wind power transmission systems based on model and data fusion, called PINN_Transformer. The method integrates the nonlinear dynamics model of the planetary gearbox as physical prior knowledge into a Transformer-based diagnostic network. To achieve this fusion, a novel physics-informed loss function is constructed, which maps the system’s vibration differential equations to the time-frequency domain via wavelet transform, and simultaneously incorporates constraints based on energy conservation and time-frequency feature matching. This approach embeds the governing physical laws directly into the learning process of the deep network, addressing the interpretability gap in purely data-driven methods while enhancing feature discrimination. Experimental results demonstrate significant advantages of PINN_Transformer compared to other advanced diagnostic methods, achieving a fault diagnosis accuracy of 99.84%. Furthermore, the model maintains robust performance with an accuracy above 97.07% under additive noise conditions, confirming its good engineering application value.