A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image

Abstract

A novel convolutional neural network namely the modified CNN-GAP model is proposed for fast fault diagnosis of the DC-DC inverter. This method improves the model structure of the traditional CNN by using a global average pooling layer to replace the fully connected layer of 2~3 layers. The improved CNN-GAP method mainly contains an input layer, a feature extraction layer, a global average pooling (GAP) layer, and a Softmax output layer. Firstly, the raw 1-D time-series data directly input into the input layer of the established CNN-GAP diagnosis model. The 2-D feature maps are reconstructed in the input layer. Secondly, the representative features are automatically extracted from the 2-D feature maps by using multiple convolutional layers and pooling layers. Thirdly, the dimension transformation and size compression of the output image of the feature extraction layer is completed by the GAP layer. Finally, the fault diagnosis result of the DC-DC inverter is automatically output in the Softmax output layer. The proposed method is used for diagnosing the open-circuit fault of the IGBT in the isolated DC-DC inverter. The proposed method is more accurate and effective than other mainstream intelligent diagnosis methods including the SVM, KNN, DNN, and traditional CNN. The experiment results show that the diagnostic accuracy is up to 99.95%, and the testing time can reduce by more than 15%. The improved CNN-GAP method could greatly reduce the model parameter quantity of the traditional CNN more than 80%, which is more suitable for rapid fault diagnosis in electronic devices.