Fault diagnosis method of wind turbine planetary gearbox based on improved CNN.

When wind turbines operate in complex environments, planetary gearboxes easily generate faults that will lead to increased wreck of the equipment or transmission failures. In this paper, a CNN model with adaptive parameters is proposed to realize the identification of planetary gearbox faults and improve the real-time performance and accuracy of fault diagnosis. Firstly, Ensemble Empirical Mode Decomposition (EEMD) is applied to scale the one-dimensional signal to solve the modal mixing problem of input data. Gramian Angular Difference Fields (GADF) is used to convert the processed data into images as the input of CNN model. Secondly, to capture more information and reduce the risk of overfitting, two convolutional neural networks incorporating different activation functions are connected in parallel to propose a multilayer CNN model structure. Additionally, Pied Kingfisher Optimization algorithm (PKO) is improved by integrating Tent chaotic mapping, second-order optimization and simulated annealing algorithm to optimize the multilayer CNN model automatically. Finally, the experimental results show that this improved model achieves real-time diagnosis due to the adaptive parameters, the diagnosis accuracy exceeds 95% under the same proportion of samples, and over 85% under the different proportions of samples. This approach significantly enhances planetary gearbox fault identification reliability.