Fault Diagnosis for Rolling Bearing Based on Improved Enhanced Kurtogram Method

被引：0
|
作者：
Tang, Guiji [1 ]
Zhou, Fucheng [2 ]
Liao, Xinghua [3 ]
机构：
[1] North China Elect Power Univ, Sch Energy Power & Mech Engn, Baoding 07003, Peoples R China
[2] North China Elect Power Univ Sci & Technol Coll, Baoding 071003, Peoples R China
[3] Hunan Goose Can Construct Grp Co Ltd, Transmiss Engn Branch, Changsha, Hunan, Peoples R China
来源：
2016 13TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI) | 2016年
关键词：
Kurtogram; Harmonic wavelet packet; Rolling bearing; Fault diagnosis; SPECTRAL KURTOSIS; VIBRATION; SIGNAL;
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：
In order to extract the fault features of rolling bearing effectively, a new improved enhanced kurtogram method is proposed. Improved enhanced kurtogram is calculated based on harmonic wavelet packet composition and the node whose kurtosis value is maximum is selected after calculating the improved enhanced kurtogram of the original fault signal, then reconstruct the signal through the harmonic wavelet packet coefficient of the optimal node, the rolling bearing fault type could be judged by analyzing the envelope spectrum of the reconstructed signal. The comparison of the proposed method with the original kurtogram method and the enhanced kurtogram method are conducted to analyze the experimental signal of rolling bearing. The results show that the new method proposed in this paper could select the resonance frequency band precisely and could be applied effectively on fault diagnosis for rolling bearing.
引用
收藏
页码：881 / 886
页数：6
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