AI 中文总结
针对强噪声下轴承故障诊断准确率下降问题，提出时频双域多尺度卷积神经网络，融合时域多尺度与频域抗噪特征，在CWRU数据集上强噪声下性能优于多个基准模型。
AI 中文摘要
为解决强噪声下轴承故障诊断准确率下降的问题，本文提出一种时频双域多尺度卷积神经网络。时域分支采用3个并行卷积核捕获多尺度脉冲特征，频域分支应用快速傅里叶变换提取抗噪的频谱结构信息。融合两个分支的特征进行故障分类，得到参数规模为110122的紧凑模型。在CWRU轴承数据集的7个信噪比水平上开展实验，结果表明，该方法在干净工况下准确率达99.75%，在-4 dB信噪比下仍保持92.50%的准确率，相比单域基准方法提升7.25个百分点，且在更强噪声下增益单调递增。 ablation实验验证了时域多尺度分支和频域分支各自的性能贡献，与WDCNN、DRSN-CW、MCNN、1D-LeNet的对比实验也证实了该方法在强噪声条件下的优越性。
英文摘要
To address the degradation of bearing fault diagnosis accuracy under strong noise, this paper proposes a time-frequency dual-domain multi-scale convolutional neural network. The time-domain branch employs three parallel convolutional kernels to capture multi-scale impulse features, while the frequency-domain branch applies the Fast Fourier Transform to extract noise-robust spectral structure information. Features from both branches are fused for fault classification, yielding a compact model of 110,122 parameters. Experiments on the CWRU bearing dataset across seven signal-to-noise ratio levels demonstrate that the proposed method achieves 99.75% accuracy under clean conditions and maintains 92.50% at -4 dB SNR, representing a 7.25 percentage-point improvement over the single-domain baseline with monotonically increasing gains under stronger noise. Ablation experiments validate the independent performance contributions of the time-domain multi-scale branch and the frequency-domain branch. Comparative experiments against WDCNN, DRSN-CW, MCNN, and 1D-LeNet confirm the superiority of the proposed method under strong noise conditions.
