基于MCABResnet的二维滚动轴承故障诊断新方法

针对时域信号冗余度大及滚动轴承故障诊断效果和强噪声环境下诊断正确率低和泛化能力差的问题，提出一种新的基于多联合注意力机制和多残差卷积块的多尺度进化故障诊断方法。采用宽、窄核卷积的跃迁块和多联合注意机制对深层卷积进行特征补充，减少特征流失，保证特征图的质量。通过通道和空间注意力权重的分配，为卷积层提供不同的权重参数，进行自适应特征细化。将提出的方法分别在凯斯西储大学轴承数据集和东南大学轴承数据集进行试验验证及分析。结果显示：所提方法的分类正确率超过99.75%，即使在强噪声环境下，分类正确率也超过98.5%；在变工况下，平均分类正确率超过了90%。因此，所提方法具有良好的故障诊断效果、泛化能力和抗噪声性能。

A New Method of Two Dimensional Rolling Bearing Fault Diagnosis Based on MCABResnet

Aiming at the problems of large redundancy of time domain signals,low diagnostic accuracy and poor generalization ability of rolling bearing fault diagnosis under strong noise environment,a new fault diagnosis method based on multi-scale evolution of multi-joint attention mechanism and multi-residual convolution block was proposed.Transition blocks of wide and narrow kernel convolutions and multiple joint attention mechanisms were used to supplement features of deep convolutions,to reduce feature loss and ensure the quality of feature maps.Through the allocation of channel and spatial attention weights,different weight parameters were provided for the convolution layer for adaptive feature refinement.The proposed method was tested and analyzed in the bearing data sets of Case Western Reserve University and Southeast University respectively.The results show that the accuracy of conventional classification is more than 99.75%,and it can also reach more than 98.5% in the case of strong noise interference.The average classification accuracy of classification in variable working conditions is more than 90%.The proposed diagnosis method has good fault diagnosis effect,generalization ability and anti noise performance.