Application of a optimized wavelet neural networks in rolling bearing fault diagnosis

被引：3

作者：

Lin Yuanyan ^{[1
]}

Wang Binwu ^{[1
]}

机构：

[1] Guilin Coll Aerosp Technol, Dept Mech Engn, Guilin 541004, Guangxi, Peoples R China

来源：

DIGITAL MANUFACTURING & AUTOMATION III, PTS 1 AND 2 | 2012年 / 190-191卷

关键词：

fault diagnosis; optimization of wavelet network; rolling bearing;

D O I：

10.4028/www.scientific.net/AMM.190-191.919

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

According to the fault type and fault signal of rolling bearing is difficult to predict, the paper proposed a new method to diagnose fault of rolling bearings with the wavelet neural network optimizated by simulated annealing particle swarm optimization. And it was applied to the fault diagnosis of rolling bearing. The experiment shows that this method can reduce the iteration time and improve the accuracy of convergence.

引用

页码：919 / 922

页数：4