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Author

Jun-Yu Lai

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Open access Jul 2026

VA-DFN: An acoustic-vibration collaborative fusion network for bearings in strong noise environments

The VA-DFN demonstrates exceptional noise-resistant robustness under varying signal-to-noise ratio (SNR) conditions from −6 dB to 2 dB, achieving a maximum diagnostic accuracy of 99.55%, which is significantly superior to existing single-modality and conventional deep learning baseline models.

Fan-Long Zhu, Jun-Yu Lai, Pei-Wen Lu et al. · 0 citations
Open access Aug 2026

PGDS-CLNet: A robust bearing fault diagnosis method under varying loads and strong noise

A novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study, which is an end-to-end trainable diagnostic network after standard signal normalization and segmentation.

Fan-Long Zhu, Jun-Yu Lai, Pei-Wen Lu et al. · 0 citations

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