双域多尺度网络提升强噪声下轴承故障诊断准确率
A Time-Frequency Dual-Domain Multi-Scale Convolutional Neural Network for Bearing Fault Diagnosis under Strong Noise
- 时间与频域并行提取多尺度冲击与谱结构特征
- -4dB信噪比下仍达92.5%准确率，优于单域方法7.25个百分点
- 适合工业设备在复杂噪声环境下的智能故障检测
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。时域分支采用三个并行卷积核捕捉多尺度冲击特征，频域分支通过快速傅里叶变换提取抗噪谱结构信息。双分支特征融合后实现故障分类，模型参数量仅110,122个。在CWRU轴承数据集上，于七种信噪比水平下实验表明，该方法在无噪条件下准确率达99.75%，-4 dB SNR下仍保持92.50%的准确率，较单域基线提升7.25个百分点，且在更强噪声下性能呈单调增长。消融实验证明时域多尺度分支与频域分支均具独立贡献。与WDCNN、DRSN-CW、MCNN及1D-LeNet对比实验进一步验证了该方法在强噪声条件下的优越性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。