Abstract:

To solve the issue that the low fault diagnosis accuracy of wind turbine gearbox, a gear fault diagnosis method based on logistic regression and genetic algorithm optimized generative adversarial networks (GAN) is proposed. The GAN model is optimized based on logistic regression and genetic algorithm. First, encoding the input signal to quantization coding by wheel selection on an acer for equipotential crossing. Then, the refactoring characterization vector is reconstructed by replacing the allelic coding string with least square variation, and the convolution network is input for the second iteration. Finally, the method builds auxiliary classifier characterization of decision boundary logistic regression. The discriminator achieves classification and diagnosis based on regression curves. Results show that the fault diagnosis accuracy of this method is up to 99.72%, which proves that the method can enhance the sample data and improve the diagnosis.

Key words: Generative adversarial network, genetic algorithm, logistic regression, least squares variation, fault diagnosis