Fault Diagnosis Based on Sparse Semi-supervised GAN Model

In this paper we propose a new fault diagnosis model based on sparse semi-supervised GAN (SSGAN).The SSGAN-based fault diagnosis can use a large amount of unmarked data to improve the accuracy of the marked training part.Solved the problem that the general neural network requires a large amount of tag data.In particular, we improved the discriminator to get a more sparse network, which further improved the classification effect.At the same time we choose Leaky ReLU as the activation function which solve the problem that the ReLU activation function has a dead zone.Simulation studies on the Tennessee-Eastman (TE) benchmark process evaluate the performance of the developed method, which indicate that the SSGAN method performs better than BPNN.