Fault diagnosis of fan gearboxes based on EEMD energy entropy and SOM neural networks

作     者：Ma, Biao Li, Gang Zheng, Guping Xu, Weifeng 

作者机构：School of Computer and Control Engineering North China Electric Power University Baoding071003 China 

出 版 物：《International Journal of Information and Communication Technology》 (Int. J. Inf. Commun. Technol.)

年 卷 期：2020年第16卷第2期

页      面：176-190页

核心收录：

学科分类：080701[工学-工程热物理] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0810[工学-信息与通信工程] 081203[工学-计算机应用技术] 0807[工学-动力工程及工程热物理] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 0801[工学-力学（可授工学、理学学位）] 

基　　金：This paper supported by National Natural Science Foundation of China (51407076)  the Fundamental Research Funds for the Central Universities (2016MS118) and the Fundamental Research Funds for the Central Universities (2015ZD28) 

主　　题：Intrinsic mode functions 

摘      要：Aiming at the difficulty of feature extraction for gear fault diagnosis and the problem of traditional classification methods cannot diagnose the faults in wind turbine gearboxes adaptively, a new fault diagnosis method based on ensemble empirical mode decomposition (EEMD) energy entropy and SOM neural networks (SOM-NN) is proposed. Firstly, the EEMD method is used to decompose the original vibration signal of the gear under all kinds of condition into several intrinsic mode functions (IMF) and calculate the energy value of each IMF and the energy entropy of the signal. Then the IMF energy proportion and the signal energy entropy are selected to form a set of features which can reflect the fault vibration signal. The values of these features are inputted to SOM neural network for classification. The numerical simulation results show that the accuracy of the method is 100% in the fault diagnosis of wind turbine gearbox. Copyright © 2020 Inderscience Enterprises Ltd.