不平衡数据集下优化WGAN的风电机组齿轮障诊断方法

针对不平衡数据集下风电机组齿轮箱故障诊断准确率低以及故障特征不明显的问题,提出峭度指标与遗传算法优化Wasserstein距离生成对抗网络（WGAN）的故障诊断方法。首先将峭度指标作为语义标签映射到卷积层规格化故障特征,其次在反卷积网络中对宏基因组二进制编码并权重初始化,然后对不平衡样本集进行多点交叉和高斯近似变异,重点搜索局部故障点,最后将峭度作为有标签的负例输入判别器网络,重构反卷积和VGG神经网络提高权重剪裁,使WGAN网络成为半监督学习模型,正向判断更新权重并输出诊断结果。实验表明：该方法在不平衡数据集下诊断准确率达到98.69%,拥有更高的泛化能力和特征提取能力,实现了故障特征的增强。

WIND TURBINE GEARBOX FAULT DIAGNOSIS METHOD FOR OPTIMIZED WGAN WITH UNBALANCED DATA SETS

To solve the problem of low-fault diagnostic accuracy and unclear fault characteristics of wind turbine gearboxes under unbalanced data sets, a fault diagnosis method of Wasserstein generative adversarial networks optimized by kurtosis label and genetic algorithm is proposed in this paper. Firstly, the kurtosis label is mapped to the convolution layer as a semantic label to normalize the fault features. Secondly, the metagenomics is binary coded and the weights are initialized in deconvolution networks. Then, multi-point crossover and gaussian approximate variation are performed on the unbalanced sample sets to search for local faults. Finally, kurtosis is inputted to the discriminator network as labeled negative cases, and deconvolution and VGG neural networks are reconstructed to improve weight cutting. The WGAN network becomes a semi-supervised learning model, which can update weight forward judgment and output diagnostic results. Experimental results showed that the diagnostic accuracy of the proposed method could reach 98.69% under unbalanced data sets, indicating that it has a higher generalization ability and feature extraction ability, which can enhance fault features.