摘要
为了提升对RV减速器的故障诊断的准确率,采用残差网络诊断RV减速器的故障。通过振动试验台测得RV减速器4种故障模式与正常模式下的振动信号,由此构造训练和测试数据集,并对训练集进行数据增强处理。然后将截取的一维信号样本预处理转换为二维信号样本,输入残差网络进行训练和5折交叉验证。接着通过残差网络的分类准确率与DNN、LeNet、10层CNN等模型的准确率进行比较,结果表明残差网络优于传统方法,对RV减速器故障的分类准确率达到了98.11%。进一步采用了西储大学轴承数据集对模型的泛用性进行验证。最终,通过LDA(线性判别分析)对残差网络平均池化层的输出进行降维,分析了散点图与RV减速器故障类型之间的关系。
In order to improve the accuracy of the fault diagnosis of the RV reducer,a residual network is used to diagnose the faults of the RV reducer.The vibration signals of four kinds of fault mode and normal mode of the RV reducer are measured through the vibration test bench,the training and test data sets are constructed with those singals,and the data of the training set was augmented.After that,with data preprocessing,one-dimensional signal samples are converted into two-dimensional signal samples and are imported in the residual network for training and 5-folds cross validation.Then,the classification accuracy of the residual network is compared with that of DNN、LeNet and 10 layers CNN.The results show that the residual network is better than the traditional methods,the classification accuracy for the RV reducer reaches 98.11%.Furthermore,the western reserve university’s bearing data set is used to verify the universality of the new model.Finally,LDA(Linear Discriminant Analysis)is used to reduce the dimension of the output of the average pooled layer of the residual network,and the relationship between the scattergram and the fault type of the RV reducer was studied.
作者
汪久根
柯梁亮
WANG Jiugen;KE Liangliang(School of Mechanical Engineering,Zhejiang University,Hangzhou 310058)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2019年第3期73-80,共8页
Journal of Mechanical Engineering
基金
国家高技术研究发展计划(863计划
2015AA043002)
国家自然科学基金(51375436)
浙江省重大科技专项(2017C01047)资助项目
