Imbalanced Process Fault Diagnosis Using Enhanced Auxiliary Classifier GAN

Abstract

Deep learning-based methods have been well ap-plied in the field of fault diagnosis when enough data can be collected. However, the available fault samples are very limited in real-industrial scenarios. In this work, an enhanced auxiliary classifier generative adversarial network(EACGAN) based data generating techniques are proposed to solve the imbalanced fault diagnosis problem. We optimize the original auxiliary classifier generative adversarial network(ACGAN) in two ways. Firstly, the boundary seeking loss is adopted to stabilize the training of ACGANs. Secondly, the cost sensitive learning is introduced to make ACGANs more suitable to solve imbalanced problems. The validity test of the proposed method is carried out using the Tennessee Eastman (TE) dataset. The experimental results reveal that the proposed enhanced auxiliary classifier generative adversarial network(EACGAN) can achieve better performance than existing GAN-based methods.