基于机匣信号的滚动轴承故障卷积神经网络诊断方法

Convolutional neural network diagnosis method of rolling bearing fault based on casing signal

针对在滚动轴承故障激励下的机匣微弱故障特征,提出了基于卷积神经网络(CNN)的故障诊断方法。利用矩阵图法、峭度图法以及小波尺度谱法3种振动信号的预处理方法,将一维原始信号转换为图像信号;利用卷积神经网络对故障进行识别。通过比较分析发现:通过连续小波尺度谱更易提取滚动轴承的故障特征,其故障识别率达到95.82%,均高于其他几种振动信号预处理方法;由于卷积神经网络可以利用深层网络结构自适应地提取滚动轴承故障特征,比传统支持向量机(SVM)方法的故障识别率高约7%。结果证明了该方法的有效性与可行性,且具有较好的泛化能力和稳健性。

A fault diagnosis method based on convolutional neural network(CNN)was proposed for the weak fault of the engine casing under the rolling bearing fault excitation.The one-dimensional original signal was converted into image signal by using three preprocessing methods:matrix graph method,kurtosis graph method and wavelet scale spectrum.Then the convolutional neural network was used to identify the fault.Through comparative analysis,the fault identification rate of rolling bearing was 95.82%,which was higher than other vibration signal pretreatment methods.At the same time,the fault recognition rate of convolutional neural network was about 7%higher than that of traditional support vector machine(SVM)because it can use deep network structure to extract the fault characteristics of rolling bearing adaptively.The results show that the proposed method is feasible and effective,and has a good generalization ability and robustness.