• CN:11-2187/TH
  • ISSN:0577-6686

›› 2011, Vol. 47 ›› Issue (11): 64-68.

• 论文 • 上一篇    下一篇

基于矢双谱的智能故障诊断方法

李凌均;韩捷;李朋勇;雷文平;董辛旻   

  1. 郑州大学机械工程学院
  • 发布日期:2011-06-05

Intelligent Fault Diagnosis Method Based on Vector-bispectrum

LI Lingjun;HAN Jie;LI Pengyong;LEI Wenping;DONG Xinmin   

  1. School of Mechanical Engineering, Zhengzhou University
  • Published:2011-06-05

摘要: 支持矢量数据描述(Support vector data description, SVDD)是一种单值分类方法,可以解决故障诊断中故障样本缺乏的问题。矢双谱方法是基于全矢谱信息融合的双谱分析方法,能够有效融合旋转机械的双通道信息,更加全面、准确地反映信号中所包含的非线性故障特征信息。为实现在缺乏故障样本的情况下,对设备故障进行有效的智能诊断,提出一种矢双谱和SVDD相结合的智能故障诊断方法。采用矢双谱对双通道信号进行处理并提取特征矢量,作为SVDD的输入参数,建立起分类模型即可对机器运行状态进行分类。将该方法应用于齿轮箱的故障诊断中,结果表明可有效提取齿轮箱信号的特征信息,提高SVDD在故障诊断中的准确度。

关键词: 全矢谱, 矢双谱, 信息融合, 支持矢量数据描述, 智能诊断

Abstract: Support vector data description (SVDD) is a kind of one-class classification method. It can be used to solve the problem of insufficient fault samples in fault diagnosis. Vector-bispectrum is the bispectrum analysis method based on the full vector spectrum information fusion. It can be used to fuse the double-channel information of the rotary machines effectively and reflect the nonlinear properties in the signals more completely and accurately. In order to realize the aim that the faults of the machines can be diagnosed effectually and intelligently under the situation of lack of fault samples, an intelligent fault diagnosis method by combining vector-bispectrum with SVDD is put forward. By using the vector-bispectrum to process the signals and extract the characteristic vectors to be used as the input parameters of SVDD. The classification model is set up and therefore the running states of the machines can also be classified. The method is applied to the gearbox fault diagnosis. The results indicate that the method can be effectively used to extract the characteristic information of the gearbox signals and increase the accuracy of SVDD in the fault diagnosis.

Key words: Full vector spectrum, Information fusion, Intelligent diagnosis, Support vector data description, Vector-bispectrum

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