Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis

IEEE Access

Jing Li¹  Michael Pecht¹  Hongjuan Ge¹  Shuwen Chen¹ 

[1] College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, CO, China;

关键词: Fault diagnosis;    convolutional neural network;    imbalanced classification;    confusable fault types;   

DOI  :  10.1109/ACCESS.2019.2954170

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown