Gas sensor fault diagnosis for imbalanced data based on generative adversarial networks

Abstract

Gas sensors play a key role in gas detection. Gas sensors are prone to failure, so they are of great significance to classify gas sensor fault. Traditional gas sensor fault diagnosis methods do not consider the condition of the imbalance of fault data. In this paper, the generative adversarial networks (GAN) method is used to generate small sample sensor fault data, balance various types of sensor fault data, and then the random forest is used for fault classification. Finally, the sensor fault data is obtained by self-made experimental system to verify the proposed method. The experimental results show that the proposed method could effectively improve the accuracy of gas sensor fault diagnosis.