基于CNN的轴承变工况故障识别系统

为了保证工业机械设备运行安全，避免轴承损伤引起的设备严重损害，实现对机械设备上滚动轴承的变工况故障诊断，设计了基于卷积神经网络的变工况滚动轴承故障诊断系统。该系统使用格拉姆矩阵方法将一维时序数据转换为二维特征图，卷积神经网络训练最大化的学习数据中的特征信息，将训练好的模型部署于LabVIEW编写的上位机中实现实时故障诊断，将所提方法在美国凯斯西储大学轴承数据中心数据集进行实验，实验验证：在美国凯斯西储大学轴承数据集上，所使用的方法变工况下无故障运行数据识别准确率达到99.85%，变工况下综合识别准确率达到91.67%，实验结果表明该方法取得了较为准确的识别效果且具有不错的泛化能力，为变工况下滚动轴承的故障诊断积累了应用经验。

Bearing fault Identification System based on CNN

In order to ensure the safe operation of industrial machinery and equipment, avoid serious equipment damage caused by bearing damage, and realize the fault diagnosis system of rolling bearings under different working conditions on mechanical equipment, a fault diagnosis system of rolling bearings under different working conditions was designed based on convolutional neural network. The system gram matrix method is used to convert one dimensional time series data for the characteristics of two-dimensional figure, convolution neural network training to maximize learning characteristic information in the data, the trained model deployed in PC written in LabVIEW real-time fault diagnosis, the method of case western reserve university in the United States bearing experiment data center data sets, experimental verification: On the bearing data set of Case Western Reserve University in the United States, the identification accuracy of the method is 99.85% and the comprehensive identification accuracy is 91.67% under different operating conditions. Experimental results show that the method achieves relatively accurate identification effect and has good generalization ability. It has accumulated application experience for fault diagnosis of rolling bearing under variable working conditions.