轴承变转速多模式下的深度卷积神经网络诊断方法研究

Study on Diagnosis Method of Deep Convolutional Neural Network under Bearing Variable Speed Conditions and Multi-Mode

针对轴承运行转速、损伤模式复杂多变的情况,传统的基于特征提取的传统故障诊断方法的效果不尽如人意。提出了一种基于深度卷积神经网络的轴承变转速多模式下的诊断方法,以振动原始数据作为网络的输入,利用卷积层进行特征提取,池化层进行特征约简,全连接层和分类器层进行故障识别。在设置合理的网络结构和参数的基础上,利用变转速多模式下的轴承故障数据建立了四分类诊断模型,其对测试数据集的诊断结果准确率达到98.6%,高于BP神经网络(72.8%)、支持向量机(76.9%)和浅层卷积网络(93.1%),表明了该方法的有效性。

In view of bearing speed conditions and damage modes are complex and changeable,and the traditional fault diagnosis methods based on feature extraction are not satisfactory,a novel diagnosis method built on deep convolutional neural network is presented.The original vibration data is used as network input in this method,the convolutional layer of this model is applied to extract the features,then play down the dimensionality of features through the pooling layer,and the full connection layer and the classifier layer are used for fault identification.On the basis of setting reasonable network structure and parameters,the four-classification diagnosis model was established by using the fault data of under variable speed conditions and multi-mode.The diagnostic accuracy of the test sets reached 98.6%,higher than that of back propagation neural network(62.8%),support vector machine(72.9%)and shallow convolutional neural network(93.1%).