Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (12): 215-217.DOI: 10.3778/j.issn.1002-8331.2010.12.064

• 工程与应用 • Previous Articles     Next Articles

Research of light-rail’s screws fault diagnosis based on SVDD

HUANG Shi-jian,YE Jun-yong
  

  1. Key Laboratory of Optoelectronic Technology & Systems,Ministry of Education,Chongqing University,Chongqing 400044,China
  • Received:2008-10-14 Revised:2008-12-26 Online:2010-04-21 Published:2010-04-21
  • Contact: HUANG Shi-jian

基于SVDD的轻轨锚固螺杆故障诊断研究

黄仕建,叶俊勇   

  1. 重庆大学 光电技术及系统教育部重点实验室,重庆 400044
  • 通讯作者: 黄仕建

Abstract: In order to implement the fault diagnosis of Chongqing light-rail’s screws,a new method of screw fault diagnosis based on Support Vector Data Description(SVDD) is proposed,which can found one-class classifier with normal screw samples only and doesn’t need feature extracting.The method solves the difficult problem of lacking fault screw samples.Compared with three familiar one-class methods,it is found that the SVDD has stronger classification ability and higher efficiency,which can distinguish the normal and abnormal screws ideally,and provides a new method for light-rail’s screws fault diagnosis.

摘要: 为了实现对重庆市轻轨轨道梁锚固螺杆的故障检测,提出了一种基于支持向量数据描述的锚固螺杆故障诊断方法,该方法只需要正常螺杆样本,且不需要对原始数据进行特征提取,就可以建立单值分类器,解决了缺少故障螺杆样本的难题。通过与常见的三种单值分类方法比较,表明SVDD分类器具有很好的分类效果和计算效率,能较好地区分正常螺杆和非正常螺杆,为轻轨锚固螺杆故障检测提供了新的诊断方法。

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