基于注意力机制与Inception-ResNet的轴承故障诊断方法

Bearing fault diagnosis method based on attention mechanism and Inception-ResNet

针对滚动轴承在噪声环境下故障难以识别的问题,提出了一种结合注意力机制与Inception-ResNet滚动轴承故障判定方法。首先提出了一种将灰度图与伪色彩处理相结合的方法,将一维振动信号转化为三维RGB图像;然后结合Inception模块与残差网络,在宽度和深度两个方面拓展网络,提高网络的表达能力;最后结合CBAM注意力机制,融合通道注意力模块与空间注意力模块,增强输入特征中更重要的特征,抑制不必要的噪声特征,从而有效提高了诊断准确率。本文采用凯斯西储大学轴承数据集进行验证,并选用几个主流的深度的学习方法进行对比试验。试验结果表明:本方法具有很好的诊断准确率,平均准确率高达99.32%,在噪声状态下进行分析实验,结果表明在噪声状态下本方法依然具有良好的准确率,验证了本方法的鲁棒性。

Aiming at the problem that rolling bearings are difficult to identify faults in noisy environments,a rolling bearing fault judgment method combining attention mechanism and Inception-ResNet is proposed.Firstly,a method combining grayscale image and pseudo-color processing is proposed to convert one-dimensional vibration signal into three-dimensional RGB image;then combined with Inception module and residual network,the network is expanded in both width and depth,and the network is improved.Finally,combined with the CBAM attention mechanism,the channel attention module and the spatial attention module are integrated to enhance the more important features of the input features and suppress unnecessary noise features,thereby effectively improving the diagnostic accuracy.In this paper,the bearing data set of Case Western Reserve University is used for verification,and several mainstream deep learning methods are selected for comparative experiments.The test results show that this method has a good diagnostic accuracy rate,the average accuracy rate is as high as 99.32%.The analysis experiment is carried out under the noise state,and the results show that the method still has a good accuracy rate under the noise state,which verifies the robustness of this method.