Fault diagnosis of gas turbine based on matrix capsules with EM routing

Abstract

The fault detection and diagnosis of a gas turbine is of great significance for guaranteeing the complicated dynamic systems working normally and safely. Most of the existing fault diagnosis methods, based on convolutional neural networks (CNN), have certain limitations in extracting correlations of multi-channel data features. The accuracy of fault diagnosis still needs to be improved. In this paper, an approach of fault diagnosis, based on matrix capsules with EM routing, is presented. First of all, three channels data, which respectively represent acceleration, pressure and pulse, are integrated into one image to feed into the network. Secondly, network models based on the matrix capsules start to be trained by using input dataset which contains fault image and normal image. Finally, the pre-trained capsules model is used to diagnose the state of testing data. Besides, to verify the superiority of the algorithm used in this paper, a comparative experiment is implemented between matrix capsule networks and CNN. The results demonstrate that the testing accuracy is 99.995%.