基于DACNN的电机滚动轴承故障诊断方法

针对强噪声、跨工况场景下数据分布差异导致传统卷积神经网络(CNN) 模型泛化性能低、诊断能力不足的问题，提出 一种基于并行卷积核和通道注意力机制的滚动轴承故障诊断方法。构造了带有不同尺度卷积核的并行网络结构，可以在抑 制噪声干扰的同时有效提取出数据中的故障特征信息；融合通道注意力机制对卷积层特征提取能力进行增强，提升模型抗噪 性能以及跨工况负载下的自适应诊断能力。利用凯斯西储大学轴承数据集训练并测试诊断效果，将该方法与其他方法进行 了性能对比。结果表明，在跨工况不同负载情况下，所提方法的诊断平均准确率为97.3%,在不同信噪比噪声干扰情况下的 诊断精度平均达93.8%,均高于其他比较方法，所提出的方法在复杂多变工况下具有良好的抗噪性能和泛化能力。

DACNN based fault diagnosis of rolling bearing in motor

In view of the problems of poor generalization ability and insuficient diagnostic capability of traditional convolutional neural network(CNN)model due to the data distribution discrepancy in strong noise environment and across working conditions,a fault diagnosis method for rolling bearings based on parallel convolution kernel and channel attention mechanism is proposed.Using this method,a parallel network structure with different convolution kernel scales was designed to effectively extract feature information from data while suppressing noise interference.Meanwhile, channel attention mechanism was added to enhance the feature extraction capability of the convolutional layer,and improve the anti-noise performance of the model and the adaptive ability in across working conditions.Diagnosis effects were trained and tested by using bearing data set of Case Western Reserve University.The proposed method was compared with peer approaches under different signal-to-noise ratio(SNR)cases and across working conditions,it was shown that the proposed method achieves an average diagnosis accuracy rate of 97.3%in across working conditions and in the variable noise experiment on the bearing dataset from Case Western Reserve University the diagnostic accuacy rate is beyond 93.8%,which are obviously higher than the competing methods;the proposed method have better noise resistance and generalization ability under complex and variable working conditions.