RA-SHViT-Net, a single-head vision transformer with additive channel-spatial attention and residual feed-forward blocks, is reported to beat several baselines for noisy rolling bearing fault classification on CWRU and Paderborn data.
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Residual Attention Single-Head Vision Transformer Network for Rolling Bearing Fault Diagnosis in Noisy Environments
RA-SHViT-Net, a single-head vision transformer with additive channel-spatial attention and residual feed-forward blocks, is reported to beat several baselines for noisy rolling bearing fault classification on CWRU and Paderborn data.