An efficient method for imbalanced fault diagnosis of rotating machinery

In industrial scenarios, accumulated sensor data collected from the working processes of rotating machinery are usually imbalanced, and there is scope for improving the diagnostic performance of existing fault diagnosis methods. To solve this problem, a novel method named the upgraded generative adversarial network (UGAN) is presented in this paper. In our method, energy-based generative adversarial networks (EBGANs) and auxiliary classifier generative adversarial networks (AC-GANs) are first combined as the main architecture due to their good sample generation and classification performance. Then, conditional variational autoencoders (CVAEs) are utilized as the generator to generate high-quality samples for orientation. Furthermore, self-normalizing convolutional autoencoders (SCAEs) are introduced into the discriminator to maintain the stability of the network and increase the capability of the network to discriminate fault samples. The experimental results on two benchmark datasets show that the proposed method possesses excellent fault diagnosis capabilities under imbalanced data conditions.

Automated summary

The study introduces a method named upgraded generative adversarial network (UGAN) that combines different types of generative adversarial networks to enhance fault diagnosis performance. Experimental results demonstrate that the proposed method exhibits excellent fault diagnosis capabilities under imbalanced data conditions.