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

作　　者：陈保家 阮宇豪 陈法法 肖文荣[1] 李公法 陶波[3] CHEN Baojia;RUAN Yuhao;CHEN Fafa;XIAO Wenrong;LI Gongfa;TAO Bo(Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance,College of Mechanical and Power Engineering,China Three Gorges University,Yichang 443002,Hubei,China;State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400030,China;Key Laboratory of Metallurgical Equipment and Control,Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]三峡大学水电机械设备设计与维护湖北省重点实验室,湖北宜昌443002 [2]重庆大学机械传动国家重点实验室,重庆400030 [3]武汉科技大学冶金装备及其控制教育部重点实验室,武汉430081

出　　处：《机械科学与技术》2024年第7期1249-1256,共8页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金项目(51975324);机械传动国家重点实验室开放基金项目(SKLMT-MSKFKT-202020);水电机械设备设计与维护湖北省重点实验室(三峡大学)开放基金项目(2020KJX02,2021KJX02,2021KJX13);武汉科技大学冶金装备及其控制教育部重点实验室开放基金项目(MECOMF2021B04)。

摘　　要：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%.

关 键 词：齿轮故障诊断 伪标签学习 抗噪性 协同训练 

分 类 号：TH132.41[机械工程—机械制造及自动化]