基于GWO-CMFH和改进ResNet轴承故障诊断

针对不同程度的小分类轴承故障，现有故障诊断方法准确率不高的问题，提出基于GWO-CMFH和改进ResNet的滚动轴承故障诊断方法。对于同一类型不同程度故障，提出基于GWO自适应优化结构元素参数的CMFH滤波方法，增强振动信号的脉冲故障特征并抑制背景噪声；采用连续小波变换将滤波后的信号转换成二维时频图谱；最后，提出基于混合注意力机制改进的残差网络模型，提高轴承故障诊断精度。在西储大学、东南大学及所选轴承数据集上进行验证实验，不同故障程度的小分类诊断准确率分别达到99.73%、98.12%和99.07%,表明所提方法具有很好的抗噪性、鲁棒性，可提高滚动轴承不同故障程度的诊断效果。

Bearing Fault Diagnosis Based on GWO-CMFH and Improved ResNet

In view of the problem that the accuracy of existing fault diagnosis methods is not high for bearing faults with different degrees of small classification,a fault diagnosis method based on GWO-CMFH and improved ResNet was proposed.For the same type of fault with different degrees,a CMFH filtering method based on GWO adaptive optimization structural element parameters was proposed to enhance the impulse fault characteristics of the vibration signal and suppress the background noise.Continuous wavelet transform was used to convert the filtered signal into a 2D time-frequency map.Finally,an improved residual network model based on the mixed attention mechanism was proposed to improve the bearing fault diagnosis accuracy.The verification experiments were carried out on Western Reserve University,Southeast University and the selected bearing datasets,and the diagnostic accuracy rates of small classification faults of different degrees reached 99.73%,98.12% and 99.07%,respectively.It shows that the proposed method has good anti-noise and robustness,and it can improve the fault diagnosis effect of rolling bearings with different degrees fault.