一种有效的不均衡样本生成方法及其在行星变速箱故障诊断中的应用

针对实际运行中行星变速箱故障数据较少、各个状态样本不均衡的问题，提出了由Wasserstein生成式对抗网络(WGAN)样本生成模型和卷积神经网络(CNN)分类模型组合的WGAN-CNN故障诊断分类模型。该模型对故障数据的频谱信号进行过采样，以扩展故障样本数量，从而更好地对故障状态进行分类。采用加州大学欧文分校人工数据集对WGAN生成模型以及经典过采样方法进行对比，并在行星变速箱故障试验台上进行验证。结果表明，样本不均衡会严重影响分类结果，而WGAN-CNN模型可以很好地扩充故障样本集，提高在故障样本稀少情况下的诊断准确率。

An Effective Method for Imbalanced Sample Generation and Its Application in Fault Diagnosis of Planetary Gearbox

A fault diagnosis classification model based on WGAN-CNN is proposed for few fault data of planetary gearbox and the imbalanced samples of each state in actual operation. The proposed model is a combination of Wasserstein generative adversarial network (WGAN), a sample generation model, a convolutional neural network (CNN), and a sample classification model. The model is used to oversample the spectral signals of fault data and expand the number of fault samples, thus classifying the fault states better. UCI artificial datasets were used to compare WGAN generation model and classical oversampling methods, and verified on a planetary gearbox fault test rig. The results show that the imbalanced samples seriously affect the classification results, and the WGAN-CNN model can well expand the fault sample datasets and improve the diagnostic accuracy in the case of rare fault samples.