优化GAN的风电机组齿轮箱故障诊断方法

Fault Diagnosis Method of Wind Turbine Gearbox by Optimized GAN

针对风电机组齿轮箱故障诊断准确率低的问题,提出了一种逻辑回归与遗传算法优化生成对抗网络(GAN)的齿轮箱故障诊断方法。该方法采用逻辑回归与遗传算法优化GAN模型,首先,输入信号向量化编码通过轮盘式选择对宏基因等位交叉;然后,用最小二乘变异替换等位编码串重构表征向量,并输入卷积网络进行二次迭代;最后,构建逻辑回归辅助分类器表征决策边界,依据回归曲线实现判别器的分类与诊断。实验结果表明,所提方法的故障诊断准确率达到99.72%,证明该方法实现了样本数据的增强和诊断准确率的提高。

Aiming at 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 method GAN model is optimized based on logistic regression and genetic algorithm,firstly,encoding the input signal to quantization coding by wheel selection on an acer for equipotential crossing,and then replaces with least⁃squares variation allelic encoded string of refactoring characterization vectors and enters the convolution network 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.