主成分分析网络(PCANet)作为一种基于深度子空间学习框架的网络模型，在多个应用领域展现出卓越的性能。然而，在滚动轴承故障诊断方面，PCANet算法存在无法准确反映数据完整结构信息、鲁棒性差以及泛化能力较弱等问题。本文针对这些问题，提出了一种基于PCANet算法和数据增强的滚动轴承故障诊断方法。该方法利用 L21范数学习滚动轴承振动信号的频域稀疏结构，从而抑制噪声数据，提高模型鲁棒性。此外，通过数据增强处理，不同类别的训练样本之间的差异性也得到显著增加，从而提高了模型的泛化能力。最后，实验结果表明，该方法明显提高了PCANet模型的鲁棒性和泛化能力，能够准确识别不同类型的滚动轴承故障。

The principal component analysis network (PCANet), as a network model based on the deep subspace learning framework, has demonstrated remarkable performance in various application domains. However, in the field of rolling bearing fault diagnosis, the PCANet algorithm suffers from issues such as inaccurate reflection of data structural information, poor robustness, and limited generalization ability. To address these issues, this paper proposes a novel rolling bearing fault diagnosis method based on the PCANet algorithm and data augmentation. The proposed method utilizes the L21-norm to learn the frequency domain sparse structure of the rolling bearing vibration signals, effectively suppressing noisy data and enhancing the robustness of the model. Moreover, through the data augmentation processing, the method significantly increases the variability between different classes of the training samples, thereby greatly improving the generalization ability of the model. Finally, experimental results demonstrate that the proposed method significantly enhances the robustness and generalization ability of the PCANet model, enabling accurate identification of different types of the rolling bearing faults.

TP277

天津市科技计划重大专项