基于优化VMD参数与VGG模型的轴承故障诊断

Bearing Fault Diagnosis Based on Optimized VMD Parameters and VGG Models

轴承振动信号的采集过程中难免会受到噪声的影响,使得轴承部分故障特征难以提取。针对此问题,提出一种基于蜣螂算法(DBO)优化变分模态分解(VMD)并与VGG神经网络相结合的轴承故障诊断方法。使用DBO对VMD进行参数寻优,经过优化后的VMD将原始振动信号分解为多个本征模态函数(IMF),通过皮尔逊相关系数选择合适的IMF对信号进行重构;对重构的信号进行连续小波变换(CWT)生成时频图;最后,通过VGG网络进行训练以完成对轴承的故障诊断分类识别。结果表明:与其他诊断方法相比,所提方法降噪效果明显,同时对轴承的故障识别准确率达到了100%。

The acquisition process of bearing vibration signals will inevitably be affected by noise,which makes it difficult to extract some of the fault characteristics of the bearing.Aiming at this problem,a new method based on the dung beetle optimizer algorithm(DBO)optimized variational mode decomposition(VMD)and combined with VGG neural network for bearing fault diagnosis was proposed.The DBO was used to optimize the parameters of VMD,and the original vibration signal was decomposed into multiple intrinsic mode functions(IMF)by the optimized VMD,and the signal was reconstructed by selecting suitable IMF through the Pearson′s correlation coefficient;the reconstructed signal was subjected to the continuous wavelet transform(CWT)to generate the time-frequency diagram;finally,the VGG network was trained to complete the classification and identification of bearing fault diagnosis.The results show that compared with other diagnostic methods,the proposed method has obvious noise reduction effect,and the fault identification accuracy of the bearing reaches 100%.