Application of wavelet packet analysis in turbine fault diagnosis

被引：0
|
作者：
Peng, Yue-Hui [1 ]
Xu, Xiao-Gang [2 ]
Zhao, He-Xiang [1 ]
机构：
[1] N China Elect Power Univ, Dept Sci & Technol, Baoding 071003, Peoples R China
[2] N China Elect Power Univ, Dept Power, Baoding 071003, Peoples R China
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
关键词：
fault diagnosis; symptom extraction; wavelet packet analysis; best tree; neural networks;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Experimental platform is used to simulate typical faults of turbine. Based on the frequency domain feature, energy eigenvector of frequency domain is presented in the wavelet packet analysis method, and the way of best tree is used to choose symptom. Finally, the fault states are recognized using neural network, and the simulations show that it makes a good performance with the method.
引用
收藏
页码：2897 / +
页数：2
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