Generative Adversarial Network With Dual Multiscale Feature Fusion for Data Augmentation in Fault Diagnosis

Performance of intelligent fault diagnosis models heavily depends on the amount of monitoring data available. In the situations of monitoring data insufficient for fault diagnosis, generative adversarial networks can augment the existing data to supplement data scarcity, which is a promising approach to improve diagnostic accuracy. However, the quality of the generated samples greatly affects the effectiveness of this method. To address this issue, this paper proposes a dual multi-scale feature fusion generative adversarial network to ensure the similarity between generated and real samples and also to improve the diversity of the generated samples. Specifically, a multi-scale feature extraction and fusion module is designed to integrate multi-scale feature extraction and fusion. A multi-scale feature decision fusion module is constructed to avoid the loss of decision-sensitive features in different healthy states. The design of the dual multi-scale feature fusion enhances the learning ability of the generation model and guarantees the similarity between the generated and real samples. A reconstruction network is established to restrain the error of the latent vectors reconstructed by the generated samples, thereby preventing the overfitting of the generated samples and improving their diversity. Experimental results demonstrate that the proposed model has advantages in the similarity, diversity, and effectiveness of the generated samples, significantly improving the performance of intelligent fault diagnosis.