摘要
为使轴承故障诊断工作更加准确与智能化,构建了一种基于Inception结构和残差结构的卷积神经网络(CNN:Convolutional Neural Network),提出一种新的轴承故障诊断方法。首先使用短时傅里叶变换(STFT:Short Time Fourier Transform)将滚动轴承原始一维信号转变为二维时频图,分为训练集、验证集和测试集;然后使用训练集对搭建的Inception-残差网络模型进行迭代,不断更新网络参数,并由验证集检验模型是否出现过拟合现象;最后将训练好的模型应用于测试集,并通过输出层的分类器输出诊断结果。最终由实验证明所提方法的可行性,对轴承故障分类的平均准确率到达了99.98%±0.02%,相对于其他方法具有较高的准确率和稳定性。
In order to make the bearing fault diagnosis more accurate and intelligent,aiming at the problem of bearing fault feature extraction,a CNN(Convolutional Neural Network)based on residual structure and Inception structure is constructed,and a new bearing fault diagnosis method is proposed.First,the STFT(Short-Time Fourier Transform)is used to transform the original one-dimensional signal of the rolling bearing into a two-dimensional time-frequency graph,which is divided into a training set,a validation set and a training set.Then the training set is used to iterate the built Inception-residual network model.The network parameters are constantly updated,and the verification set is used to check whether the model has over-fitting phenomenon.Finally,the trained model is applied to the test set,and the diagnosis result is output through the classifier of the output layer.The experiments proved the feasibility of the proposed method,and the average accuracy of bearing fault classification reached 99.98%±0.02%,which has a higher accuracy and stability than other methods.
作者
任爽
林光辉
田振川
商继财
杨凯
REN Shuang;LIN Guanghui;TIAN Zhengchuan;SHANG Jicai;YANG Kai(College of Electrical Information and Engineering,Northeast Petroleum University,Daqing 163318,China)
出处
《吉林大学学报(信息科学版)》
CAS
2022年第4期621-627,共7页
Journal of Jilin University(Information Science Edition)
基金
东北石油大学科研基金资助项目(2019YDL-10)。