基于二维深度卷积网络的旋转机械故障诊断

ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK

针对旋转机械振动信号复杂且难以提取有效故障特征的问题,提出了一种短时傅里叶变换和二维深度卷积网络相结合的故障诊断方法。首先对旋转机械振动信号进行短时傅里叶变换,得到时频图;接着将时频图输入到二维深度卷积网络中进行识别,得到最终分类结果。将该方法分别应用于滚动轴承与齿轮箱故障诊断中,在凯斯西储大学滚动轴承数据集、PHM2009直齿齿轮箱数据集上均取得了较好效果,正确率优于将时域信号直接输入到经典CNN中,验证了该方法的优越性。

In order to extract effective features of complex signals,a fault diagnosis method combining short-time Fourier transform and two-dimensional convolution neural network is proposed.First,a short-time Fourier transform is performed on the rotating mechanical vibration signal to obtain a time-frequency map.Then,the time-frequency map is input into a two-dimensional convolution neural network for identification,and a final classification result is obtained.The method is applied to the fault diagnosis of rolling bearing and gearbox,and has achieved good results in the data of the Case Western Reserve University and the PHM2009 dataset.The correct rate is better than the direct comparison of the original signal into CNN,which verifies the superiority of the method.