IMF能量和RBF神经网络相结合在滚动轴承故障诊断中的应用研究
张梅军,王闯,陈灏
(中国人民解放军理工大学,江苏南京 210007

 摘要针对滚动轴承的故障特点,提出了一种将IMF能量与RBF神经网络相结合的方法用于故障诊断。该方法首先利用经验模态分解(EMD)方法,把振动信号分解为若干个IMF分量,再用重要的IMF分量求得IMF能量特征向量,最后将特征向量输入RBF神经网络进行故障模式分类。通过对滚动轴承的正常状态、内圈故障、滚动体故障和外圈故障信号的分析结果表明,该方法能够准确、有效地识别这些故障。

关键词IMF能量;RBF神经网络;故障诊断
中图分类号TH17         文献标识码A              文章编号10060316 (2012) 06006305
The application research on the combination of IMF energy and RBF neural network
in rolling bearing fault diagnosis
ZHANG Mei-junWANG ChuangCHEN Hao
(PLA University of Science And Technology, Nanjing210007, China )

 AbstractAccording to the characteristics of rolling bearing fault,This paper put forward a fault diagnosis method combining IMF energy and RBF neural network. This method firstly use the empirical mode decomposition (EMD) method to decompose the vibration signal into some IMF component, then garnish with important IMF component to obtain characteristic vector of IMF energy , finally put feature vector into RBF neural network fault to classify the fault pattern.Through the signal analysis of the normal state, inner ring fault, roller ring fault and outer ring fault , showing that the method can accurately, effectively identify these fault.

Key wordsIMF energyRBF neural networkfault diagnosis


———————————————
收稿日期:2011-12-27
基金项目:国家自然科学基金资助项目(51175511)
作者简介:张梅军(1958-),女,江苏宜兴人,副教授,硕士生导师,主要研究方向为故障诊断和工程机械动力学等;王闯(1988-),河南新乡,硕士研究生,主要研究方向液压系统故障诊断。

 

设为首页  |  加入收藏    |   免责条款
《机械》杂志版权所有     Copyright©2008-2012 Jixiezazhi.com All Rights Reserved 

  电话:028-85925070    传真:028-85925073    E-mail:jixie@vip.163.com

地址:四川省成都锦江工业开发区墨香路48号   邮编:610063

蜀ICP备08103512号

Powered by PageAdmin CMS