半监督TS网络模型在齿轮故障诊断中的应用

Application of Semi-supervised Teacher-Student Network Model in Gear Fault Diagnosis

为解决在工业大数据条件下,有标签样本少导致机械故障诊断准确率低的问题,提出了一种半监督神经网络模型。该方法采用协同训练的方式,从时域和频域两个维度训练教师网络(T),将无标签数据转化为高质量的伪标签数据。再利用转化后的伪标签数据训练学生网络(S),通过对数据进行评判和计分,避免网络过拟合。最后通过得分函数,对伪标签数据进行阶梯筛选成为有标签数据。齿轮故障诊断结果表明:TS网络在仅有少量有标签数据的情况下,故障分类准确率达90.31%,与其他半监督方法相比,准确率高出15%~20%。在信噪比(SNR)为5、0、-5的条件下,模型可以达到86.81%、78.00%、52.78%的诊断准确率。

In practical industrial applications,the fault diagnosis accuracy of mechanical equipment is low due to the small amount of labeled data.A TS neural network model based on semi-supervised learning strategy is proposed to solve this problem.Firstly,the teacher network(T)is trained by co-training from both the time domain and frequency domain,then it is used to convert unlabeled data into high-quality pseudo-labeled data.Secondly,the pseudo-labeled data is used to train the student network(S)and avoid network overfitting by judging and scoring the data.Finally,the pseudo-label data is filtered into the labeled data through the scoring function.The results of gear fault diagnosis show that the TS network has a fault classification accuracy of 90.31%,which is 15%-20%higher than other semi-supervised methods.Under the condition of SNR of 5,0 and-5,the model can achieve diagnostic accuracy of 86.81%,78.00%and 52.78%.