基于时频图像的齿辊式破碎机中电机轴承故障特征提取方法

Bearing fault feature extraction of roller crusher motor based on time-frequency image

齿辊式破碎机中实际运行中电机轴承的振动信号耦合严重,故障脉冲往往显得微弱,故障识别难度大,特别是滚动体和内圈故障的区分。将图像纹理特征提取技术引入故障诊断,提出一种基于时频图像的轴承故障特征提取方法。首先,将聚合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)加入Wigner-Ville时频分析(Wigner-Ville Distribution,WVD)中获得无交叉项干扰的振动时频表征;其次,引入局部二值模式(Local Binary Patterns,LBP)增强时频灰度图像纹理特征,生成对应的LBP谱图;接着,以LBP灰度直方图作为特征量,压缩特征维数后利用主成分分析法(Principal Components Analysis,PCA)约减特征量;最后,将低维特征量输入BP神经网络进行故障分类。在轴承故障诊断实验中,通过和其他算法的对比分析验证了该方法具有较高的故障识别精度,且99.5%的精度充分说明了方法有效性,为准确提取齿辊式破碎机中电机的轴承故障特征提供了一种可靠手段。

The vibration signals of rolling bearings in the actual operation of roller crusher are seriously coupled,and the fault pulse tends to be weak.Thus,the fault identification becomes difficult,especially,in the fault distinguish between the rolling body and the inner ring.A feature extraction method of bearing fault based on time-frequency image has been put forward by introducing the image texture feature extraction technology to the fault diagnosis.Firstly,combing the Ensemble Empirical Mode Decomposition(EEMD)with Wigner-Ville Dis-tribution(WVD)can obtain the vibration frequency representation with no cross-term interference.Secondly,the corresponding Local Binary Patterns spectrum can be generated by introducing the LBP,which help to enhance the texture features in time-frequency grayscale images.Thirdly,the LBP gray histogram can be taken as feature quantity,and reduced by means of PCA after compressing the feature dimension.Finally,the low-dimensional characteristic quantity can input into BP neural network for fault classification.In the experiment of bearing fault diagnosis,the method proposed above is proved to have a high fault recognition accuracy through comparing with other algorithms,and its accuracy of 99.5%fully demonstrates the effectiveness of the method.This method is reliable to accurately extract the bearing fault characteristics of motor in the gear roller crusher.