Power converter fault classification method based on multi-feature selection algorithm - CHI Chengzhi; PAN Zhen; XU Zhao; ZHANG Yitong (Xibei Gongye Daxue Xuebao, 2022)
In the process of fault diagnosis for the core components of the integrated modular avionics power conversion module, selecting appropriate features can effectively improve the efficiency and classification accuracy of the model, and greatly reduce the computational complexity of the learning algorithm. This paper first designs a typical Sepic structure DC-DC converter model to simulate the typical fault types of the DC-DC converter; secondly, the corresponding original data is obtained through simulation; after data preprocessing, feature extraction and using multiple feature selection fusion algorithm, BP neural network method is used finally for fault diagnosis analysis of DC-DC converter. The simulation verifies the effectiveness of the above method.