T-type inverter fault diagnosis based on GASF and improved AlexNet

Abstract

Aiming at the problems of high similarity of T-type inverter open-circuit fault features, cumbersome manual extraction of fault features, and the inability of one-dimensional convolutional neural network to fully play the role of feature extraction. In this paper, an end-to-end fault diagnosis method is proposed, which is based on Gramian Angular Summation Field and improved AlexNet network. The fault results can be diagnosed by collecting only the single-phase line voltage. Firstly, the collected one-dimensional timing signal is mapped into two-dimensional images through the Gram summation angle field algorithm. Then feature extraction is performed by the improved AlexNet to take advantage of its ability to extract image features. Finally, the fault diagnosis result is output by the Softmax layer. Simulation experiments show that the method proposed in this paper can automatically extract features that are helpful for fault identification from raw data. The fault diagnosis rate of this model is as high as 99.72%, and it can diagnose not only a single fault, but also multiple faults in different phases. Compared with other methods, the proposed method shows a better fault feature extraction effect and higher fault diagnosis accuracy.