A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise  

作　　者：Tichun WANG Jiayun WANG Yong WU Xin SHENG 

机构地区：[1]College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China [2]Department of Business Strategy and Innovation,Griffith University,Gold Coast Campus,QLD 4222,Australia [3]School of Business,Jiangsu Open University,Nanjing 210036,China

出　　处：《Chinese Journal of Aeronautics》2020年第10期2757-2769,共13页中国航空学报（英文版）

基　　金：the National Natural Science Foundation of China(No.51775272,No.51005114);The Fundamental Research Funds for the Central Universities,China(No.NS2014050)。

摘　　要：In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis models.To address these issues,this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network(W-ENN).WENN is a novel neural network which has three types of connection weights and an improved correlation function.The performance of the proposed model is validated against Extension Neural Network(ENN),Support Vector Machine(SVM),Relevance Vector Machine(RVM)and Extreme Learning Machine(ELM)based models.The results indicate that,on noisy small sample sets,the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability.The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.

关 键 词：Fault diagnosis Samples with noise Small samples learning Turbo-generator sets Weighted Extension Neural Network 

分 类 号：TM311[电气工程—电机] TP183[自动化与计算机技术—控制理论与控制工程]