关键词: 智能故障诊断, 卷积神经网络, 支持向量机, DC-DC变换器, 开路故障

Abstract: In recent years,intelligent fault diagnosis methods based on deep learning technology have be.come a research hotspot in the field of electrical converters.Convolutional Neural Network (CNN) has the potential to identify early-term micro faults because of its powerful feature extraction capabilities.The current Convolutional Neural Network (CNN) algorithms are not suitable for the rapid diagnosis of electrical faults due to its complicated model structure,excessive training parameters and long diagnosis waiting time.For this reason,a new deep learning algorithm based on improved.cnN-GAP-SVM was proposed for the rapid diagnosis of DC-DC converters early faults.The model input layer automatically transformed the raw 1D time-series monitoring data into 2D feature map training samples.To reduce model parameters,a Global Average Pooling Layer (GAP) was designed to improve the fully connected network structure of 2~3 layers in the traditional.cnN.To further improve the diagnosis accuracy,a nonlinear Support Vector Machine (SVM) was used to replace the traditional Softmax function as the final classifier.Experiments showed that the proposed method could improve the diagnostic accuracy to 100% and the diagnosis speed by 23%.compared with the traditional diagnosis methods,the proposed method had faster diagnosis speed and higher diagnosis accuracy.

Key words: intelligent fault diagnosis, convolutional neural network, support vector machine, DC-DC converter, open-circuit fault