Data Imbalance Bearing Fault Diagnosis Based on Fusion Attention Mechanism and Global Feature Cross GAN Network

Abstract As one of the important equipment of motor transmission, bearings play an important role in the production and manufacturing industry, if there are problems in the manufacturing process will bring significant economic losses or even endanger personal safety, so its state prediction and fault diagnosis is of great significance. In bearing fault diagnosis, it is a challenge to eliminate the effect of data imbalance on fault diagnosis. Generative adversarial network (GAN) networks have achieved some success in data imbalance fault diagnosis, but GAN networks suffer from sample generation bias when balancing samples. To solve this problem, fusion attention mechanism and global feature cross GAN networks is proposed. Firstly, the spatial channel fusion attention mechanism is added to the generator, so that the generator selectively amplifies and processes sample features from different regions, which helps the generator learn more representative features from a few categories; secondly, the global feature cross module is added to the discriminator, so that the discriminator efficiently extracts features from different samples, and improves its ability of recognizing the sample discrepancy; at the same time, in order to improve the model’s anti-noise ability, an anti-noise module is added to the discriminator to improve the efficiency of the model’s data imbalance fault diagnosis; finally, this paper’s method is validated by using two public bearing datasets and one self-constructed dataset. The experimental results prove that this method can effectively overcome the effect of data imbalance on GAN networks, and has a high accuracy rate in real industrial fault diagnosis tasks, what’s more, it proves that the method in this paper has a very good anti-noise performance and practical application value.