Shuwen Chen, 1, Honjuan Ge, 1, Jing Li, 1, and Michael Pecht 2
1 College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing CO 211106, China
2 Center for Advanced Life Cycle Engineering, University of Maryland at College Park, College Park, MD 20742, USA
Among deep learning methods, convolutional neural networks (CNNs) are able to extract features automatically and have increasingly been used in intelligent fault diagnosis studies. However, studies seldomly concentrate on the weakness associated with a highly imbalanced distribution of fault types due to different failure rates and when multiple faults are easily confused with single faults. To solve these problems, this paper developed a stochastic discrete-time series deep convolutional neural network (SDCNN) method based on random oversampling along with a progressive method with multiple SDCNNs to improve the diagnosis performance. To assess the developed method, datasets from three avionics 24-pulse auto-transformer rectifier units (ATRUs), which are secondary electric power supplies in aircraft, were analyzed and compared with other CNN methods.
NOTICE: A correction has been issued regarding the funding and the introduction of section organization in Section 1. View the corrected information online here.