|本期目录/Table of Contents|

[1]杨青,王栗,刘彧诚,等.FICA-IPNN 集合型滚动轴承故障诊断方法[J].电机与控制学报,2014,18(03):73-78.
 YANG Qing,WANG Li,LIU Yu-cheng,et al.FICA-IPNN ensemble fault diagnosis approach of rolling bearing[J].,2014,18(03):73-78.
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FICA-IPNN 集合型滚动轴承故障诊断方法(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
18
期数:
2014年03
页码:
73-78
栏目:
出版日期:
2014-03-15

文章信息/Info

Title:
FICA-IPNN ensemble fault diagnosis approach of rolling bearing
作者:
杨青 王栗 刘彧诚 刘念
1. 沈阳理工大学信息科学与工程学院,辽宁沈阳110159; 2. 北京理工大学机械与车辆学院,北京102488
Author(s):
YANG Qing WANG Li LIU Yu-cheng LIU Nian
1. School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China;
2. School of Mechanical and Vehicle Engineering,Beijing Institute of Technology,Beijing 102488,China
关键词:
故障诊断 快速独立成分分析 增量概率神经网络 特征提取 滚动轴承
Keywords:
fault diagnosis fast independent component analysis incremental probabilistic neural networkfeature extract rolling element bearing
分类号:
-
DOI:
-
文献标志码:
A
摘要:
为了提高滚动轴承故障诊断的准确性和适应性,提出快速独立成分分析( fast independent
component analysis,FICA) 和增量概率神经网络( incremental probabilistic neural network,IPNN) 相
结合的FICA - IPNN 集合型滚动轴承故障诊断方法。首先,针对滚动轴承的故障振动信号非高斯
特点,利用固定点迭代的FICA 算法提取出滚动轴承振动信号特征,其次,为了提高概率神经网络
分类的适应性,采用在线增量方法,优化概率神经网络结构,训练概率神经网络参数。实验表明,该
集合型故障诊断方法较传统概率神经网络有更高的分类准确性和适应性。
Abstract:
An ensemble approach based on fast independent component analysis ( FICA) and incremental
probabilistic neural network,called FICA-IPNN,was proposed to improve the accuracy and adaptability
of rolling bearing fault diagnosis. Firstly,the feature of the vibration signals of the rolling bearing,usually
non-Gaussian,was extracted by the fixed-point iteration FICA algorithm. Then the online incremental
method was adopted to optimize the probabilistic neural network structure and train probabilistic neural
network parameters to improve the classification adaptability of probabilistic neural network. The experimental
results show that the accuracy and adaptability of classification by FICA-IPNN are better than that
of traditional probabilistic neural network.

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备注/Memo

备注/Memo:
更新日期/Last Update: 2014-11-24