非下采样轮廓波变换在故障分类中的应用
Investigation on Fault Classification Based on Non-subsampled Contourlet Transform

基于非下采样轮廓波变换的多尺度分解和多方向分解的特性，提出一种用于时频图像特征提取的方法。首先，将振动信号变换到时频域得到时频图像，并利用Matlab将得到的时频图像转换为灰度图像；其次，对该图像进行非下采样轮廓波变换，得到其高频和低频子带，根据高频子带和低频子带所包含信息不同，研究不同的特征提取方法，笔者提取高频子带的能量和低频子带的均值、标准差作为特征值；最后，利用支持向量机（support vector machine, 简称SVM）对齿轮箱的不同程度故障以及滚动轴承故障进行分类测试。实验结果验证了该方法提取时频图像特征量的有效性，为设备的状态识别提供了一种有效的方法。
In this research, a new method of time-frequency image feature extraction is proposed. It is based on the multi-scale and multi-direction decomposition of the non-subsampled contourlet transform. Firstly, the vibration signals are transformed into a time-frequency image which is then converted into a gray image based on the contourlet transform. Then, the coefficients at high and low frequency are calculated based on the gray image. Different feature extraction methods are investigated in details. In this paper, the energy at the high-frequency, the mean and standard deviation at the low-frequency are calculated as characteristic parameters. Finally, the data of different conditions from a gearbox and rolling bearing are classified and tested by support vector machine (SVM). The results show that the proposed method is effective in determining the characteristic value of a time-frequency image for the condition identification.