基于SCGAN网络的齿轮故障诊断方法

Fault Diagnosis Method of Gear Based on SCGAN Network

为了提高齿轮箱中齿轮单故障及复合故障的识别精度,克服传统故障特征提取方法过于依赖经验判断的困难,从深度学习领域出发,融合卷积神经网络(convolutional neural network,简称CNN)与对抗神经网络(generative adversarial network,简称GAN)两种深度神经网络特征,提出一种半监督卷积对抗神经网络模型(semi-supervised convolutional generative adversarial network,简称SCGAN)。采用两个CNN网络分别作为GAN网络的生成网络(G)和判别网络(D),改进了网络结构,实现了GAN由无监督学习机制向半监督学习机制的转变。将动力传动模拟试验台上采集的齿轮故障信号制成时域、频域和时频样本集,构建SCGAN模型用于故障诊断。对比3种不同种类的网络模型,结果表明,在不同的样本类型和不同的样本大小下,SCGAN的诊断精度明显高于CNN与RNN,且收敛速度快。

In order to improve the recognition accuracy of single faults and compound faults for gear in different gearboxes,and overcome the difficulty of traditional fault feature extraction methods relying too much on empirical judgment,a semi-supervised convolutional generative adversarial network(SCGAN)model is proposed based on deep learning,which combines two deep neural network features,convolutional neural network(CNN)and generative adversarial network(GAN).This model uses two CNN networks as the generation network(G)and the discriminant network(D)of the GAN network,and improves its internal structure to realize the transformation of GAN from an unsupervised learning mechanism to a semi-supervised learning mechanism.The gear fault signals collected in the power transmission simulation test-bed are made into time-domain,frequency-domain and time-frequency sample sets,and the SCGAN model is constructed for fault diagnosis.By comparing the effects of several situations,including different network model,different sample set and different iteration times,the results show that the diagnostic accuracy of the SCGAN is significantly higher than that of CNN and recurrent neural network(RNN),and the convergence speed is faster.