Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (1): 225-228.

• 可靠性 • 上一篇    下一篇

基于神经网络组与故障分级的故障诊断

马成才, 顾晓东   

  1. 复旦大学电子工程系, 上海, 200433
  • 收稿日期:2007-08-14 修回日期:2008-05-30 出版日期:2009-01-20 发布日期:2010-01-03
  • 作者简介:马成才(1983- ),男,硕士研究生,主要研究方向为人工智能,图像压缩编码.E-mail:062021046@fudan.edu.cn
  • 基金资助:
    国家自然科学基金(60671062;60571052);国家重点基础研究发展计划(2005CB724303)资助课题

Fault diagnosis with fault gradation using neural network group

MA Cheng-cai, GU Xiao-dong   

  1. Dept. of Electronic Engineering, Fudan Univ., Shanghai 200433, China
  • Received:2007-08-14 Revised:2008-05-30 Online:2009-01-20 Published:2010-01-03

摘要: 为了更好地解决当前神经网络在故障诊断方面的不足,提高诊断的精度和正确率,提出了一种基于神经网络组和故障分级思想的故障检测方法,在将故障分级的同时使用一个包含着三个子神经网络的神经网络组来完成故障检测。根据故障发生频率的不同,将故障分成了不同的等级。故障等级越高,用于检测这种故障的子神经网络数越多,以此来保证较高的故障检测正确率。实验结果表明:对于等级最高的故障,检测正确率是100%;对于其他故障,检测正确率也都在95%左右。实验结果充分证明了此方法在故障检测方面的优越性。

Abstract: In order to resolve the shortage of the neural network fault diagnosis and improve the precision and veracity,a new fault diagnosis approach with fault gradation using neural network group consisting of 3 sub neural networks is proposed.The faults different grades are given according to the occurrence frequencies of different faults.The higher the fault grade,the larger the number of the used sub neural networks is.Experimental results show that the diagnosis correctness rate of the faults with the highest grade is 100%,and the diagnosis correctness rates of the other faults with lower grades,are about 95%.The proposed approach is of better performance.

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