Fault Feature Extraction of Gearboxes Using Ensemble Empirical Mode Decomposition

被引：0
|
作者：
Lin, Jinshan [1 ]
机构：
[1] Weifang Univ, Sch Mech & Elect Engn, Weifang, Shandong, Peoples R China
来源：
2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL I | 2010年
关键词：
feature extraction; gearbox; emsemble empirical mode decomposition(EEMD); intrinsic mode function(IMF);
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper employs ensemble empirical mode decomposition (EEMD) to extract the fault information from the signal collected from a defective gearbox. In view of the shortcoming of the mode mixing which empirical mode decomposition (EMD) fails to overcome, the EEMD method is used to decompose the signal captured from the defective gearbox and successfully separate the different components from high frequency to low frequency. Then, the first four intrinsic mode functions (IMFs), containing the most energy of the signal, are extracted; by analyzing the spectrum of the first four components, we succeed in uncovering the reason causing the fault of the gearbox. The results show that the EEMD method could be feasible to diagnose the fault of the gearbox.
引用
收藏
页码：271 / 274
页数：4
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