Open access
Oct 2026
Fault diagnosis method based on convolutional attention mechanism and ResNet34
A fault-diagnosis method that integrates a least-squares-loss and weight-normalization deep convolutional generative adversarial network (LW-DCGAN) with a convolutional-attention-enhanced 34-layer residual network (C-ResNet34) to improve fault-feature extraction and demonstrate the effectiveness of the proposed method.
Qi-Zhi Zhang, Xue-Nian Hu, Xu-Jing Tian et al.
· Engineering Research Express · 0 citations