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<p>Vibration signals collected from harsh environments often suffer from severe noise interference, which can submerge critical fault characteristics. To address this challenge, a frequency attention network based on multi-scale parallel ResNet (MSPResNet-FA) is proposed. The architecture integrates a Fast Fourier Transform Convolution (FFT-Conv) module to extract harmonic frequency impulse feature, while parallel multi-scale kernels in the convolutional layer are employed to extract various scale local frequency features. Furthermore, a Convolutional Block Attention Module (CBAM) is utilized to enable the network to focus on and extract important features from the frequency domain especially the multi-scale harmonic frequency impulse feature. The effectiveness of MSPResNet-FA was validated using four benchmark datasets: Politecnico di Torino (PDT), Xi’an Jiao Tong University (XJTU), Paderborn University (PU), and the Society for Machinery Failure Prevention Technology (MFPT). Experimental results indicate that MSPResNet-FA outperforms current state-of-the-art methods, notably achieving accuracy gains of over 5% on the XJTU, MFPT, and PDT datasets. Ablation studies confirm that the FFT component is foundational to this high performance, contributing an improvement of over 10%, while the CBAM refinement layer provides an additional 1% enhancement.</p>

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Multi scale parallel frequency attention network for bearing fault diagnosis under severe noise

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Jungan Chen,
  • Xu Chen,
  • Hang Wang,
  • Ang Lv

摘要

Vibration signals collected from harsh environments often suffer from severe noise interference, which can submerge critical fault characteristics. To address this challenge, a frequency attention network based on multi-scale parallel ResNet (MSPResNet-FA) is proposed. The architecture integrates a Fast Fourier Transform Convolution (FFT-Conv) module to extract harmonic frequency impulse feature, while parallel multi-scale kernels in the convolutional layer are employed to extract various scale local frequency features. Furthermore, a Convolutional Block Attention Module (CBAM) is utilized to enable the network to focus on and extract important features from the frequency domain especially the multi-scale harmonic frequency impulse feature. The effectiveness of MSPResNet-FA was validated using four benchmark datasets: Politecnico di Torino (PDT), Xi’an Jiao Tong University (XJTU), Paderborn University (PU), and the Society for Machinery Failure Prevention Technology (MFPT). Experimental results indicate that MSPResNet-FA outperforms current state-of-the-art methods, notably achieving accuracy gains of over 5% on the XJTU, MFPT, and PDT datasets. Ablation studies confirm that the FFT component is foundational to this high performance, contributing an improvement of over 10%, while the CBAM refinement layer provides an additional 1% enhancement.