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The paper presents PerDCGAN, a novel data augmentation framework utilizing a Perceptually Optimized Deep Convolutional GAN for high-fidelity bearing fault diagnosis, addressing challenges posed by data imbalance in deep learning models. By integrating a perceptual loss function from a pre-trained VGG-16 network, the method preserves critical structural details and high-frequency patterns in time-frequency spectrograms, significantly improving diagnostic accuracy in industrial applications. Experimental resu
- Author
- M8Z
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- EN