Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis

Rolling bearing fault diagnosis is of great significance to the stable operation of rotating machinery systems. However, the fault data collected in practical engineering is seriously imbalanced, which degrades the diagnosis performance. In this paper, a novel data synthesis method called deep feature enhanced generative adversarial network is proposed to improve the performance of imbalanced fault diagnosis. Firstly, to avoid the mode collapse phenomenon and improve the stability of the generative adversarial networks, a pull-away function is integrated to design a new objective function of the generator. Secondly, a self-attention module is utilized in the networks to enhance the deep features of real signals, thereby the quality of synthesized data is improved. Finally, an automatic data filter is established to timely ensure the accuracy and diversity of synthesized samples. Experiments are implemented on two rolling bearing datasets. The results indicate that the proposed method outperforms other intelligent methods and shows great potential in imbalanced fault diagnosis.

Automated summary

A novel data synthesis method called deep feature enhanced generative adversarial network is proposed in this paper to improve the performance of imbalanced fault diagnosis. By integrating a pull-away function, a self-attention module, and an automatic data filter, the quality of synthesized data is improved, the stability of generative adversarial networks is enhanced, and the accuracy and diversity of synthesized samples are timely ensured.