【摘要】 为提高轴承故障诊断精度并解决多分类数据不均衡的问题，提出一种基于频域输入与数据增强的改进卷积神经网络(data augmentation modified convolutional neural network, DAMCNN)的轴承故障诊断算法。首先，将轴承采样的时域数据转换为频域数据，来提高特征的显著程度；然后，通过重叠滑窗采样策略扩充样本，改善数据的不均衡问题；最后，使用凯斯西储大学的轴承数据集通过仿真实验测试所提算法的性能，实验结果表明，相比于其他多分类算法，所提出的DAMCNN算法显著提升了不均衡数据多分类任务的精度和召回率。更多还原

【Abstract】 To improve the accuracy of bearing fault diagnosis and address the issue of multi-class data imbalance, a bearing fault diagnosis algorithm based on frequency-domain input and data augmentation modified convolutional neural network(DAMCNN) is proposed. Firstly, the sampled time-domain bearing data are converted into frequency-domain data to enhance feature distinctiveness. Secondly, an overlapping sliding window sampling strategy is applied to expand the dataset and mitigate the data imbalance. Finally, the performance of the proposed algorithm is evaluated through simulation experiments using the Case Western Reserve University(CWRU) bearing dataset. Experimental results demonstrate that, compared with other multi-class algorithms, the proposed DAMCNN algorithm significantly improves the precision and recall in imbalanced data multi-class classification tasks.更多还原