Soft Fault Diagnosis for DC–DC Converter Based on Improved ResNet-50

Abstract

DC-DC converter is the vital part of the power system, and its fault can cause the failure of complex electronic equipment. Therefore, the timely and accurate fault diagnosis of DC-DC converter is particularly important. This paper proposes a DC-DC converter fault diagnosis method based on improved SE-ResNet algorithm. First, in order to extract all information about the signals, we transform the one-dimensional signal into Gramian angular differential field (GADF) images. Then, the Squeeze-and-Excitation (SE) network is added to ResNet-50, combined with h-swish function and label smoothing cross entropy loss function (LSCE) to improve the model performance. In addition, simplify the redundancy layer of the network and achieve lightweight to improve the training efficiency. In the end, the logsoftmax is used for evaluating the effectiveness of the proposed method. The simulation experimental accuracy is up to 99.3220%,which is higher than other five classical deep learning algorithms. The hardware experiment also denotes the engineering practicability of the proposed method.

Keywords

• DC–DC converter
• deep learning
• fault diagnosis
• ResNet-50