FE-MCFormer, a time-frequency transformer-style network with a learnable Fourier reconstruction layer, achieves state-of-the-art fault diagnosis accuracy on three rotating machinery datasets under strong noise.
Weak fault detection of rolling bearing using a ds-based adaptive spectrum reconstruction method
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FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments
FE-MCFormer, a time-frequency transformer-style network with a learnable Fourier reconstruction layer, achieves state-of-the-art fault diagnosis accuracy on three rotating machinery datasets under strong noise.