KPCA和耦合隐马尔科夫模型在轴承故障诊断中的应用

刘韬;陈进;董广明

振动与冲击 ›› 2014, Vol. 33 ›› Issue (21) : 85-89.

PDF(1490 KB)
PDF(1490 KB)
振动与冲击 ›› 2014, Vol. 33 ›› Issue (21) : 85-89.
论文

KPCA和耦合隐马尔科夫模型在轴承故障诊断中的应用

  • 刘韬1,2,陈进2,董广明2
作者信息 +

Application of KPCA and coupled hidden Markov model in bearing fault diagnosis

  • Liu Tao1,2, CHEN Jin2, Dong Guang-ming2
Author information +
文章历史 +

摘要

针对多通道数据的有效融合能够更加准确地诊断轴承的故障,提出了一种基于KPCA和耦合隐马尔可夫模型(CHMM)的轴承故障诊断方法。首先,分别对轴承各通道的振动信号进行特征提取,获得特征向量。然后采用KPCA对各通道的特征向量分别进行特征约减,获取主要的信息成分。最后,利用CHMM对多通道信息进行融合和故障诊断。通过对滚动轴承在正常、内圈故障、外圈故障和滚动体故障状态下实验数据的分析表明,该方法能够更加有效地诊断轴承的故障。

Abstract

The fusion of multi-channels bearing monitoring information can obtain more accurate result in bearing fault diagnosis. Therefore, a rolling element bearing fault diagnosis scheme based on KPCA and coupled hidden Markov model (CHMM) is presented. At first, the features are extracted from bearing vibration signal from multi-channels respectively. Then, the KPCA is utilized to reduce the feature dimension. At last, the new KPCA feature is input into CHMM to train and diagnose the bearing fault. The data acquired from bearing at normal condition, inner race fault, outer race fault and rolling body fault are analyzed. And the results demonstrate the effectiveness and validity of the proposed method.

关键词

核主成分分析 / 耦合隐马尔可夫模型 / 滚动轴承 / 故障诊断

Key words

KPCA / CHMM / rolling element bearing / fault diagnosis

引用本文

导出引用
刘韬;陈进;董广明. KPCA和耦合隐马尔科夫模型在轴承故障诊断中的应用[J]. 振动与冲击, 2014, 33(21): 85-89
Liu Tao;CHEN Jin;Dong Guang-ming. Application of KPCA and coupled hidden Markov model in bearing fault diagnosis[J]. Journal of Vibration and Shock, 2014, 33(21): 85-89

PDF(1490 KB)

Accesses

Citation

Detail

段落导航
相关文章

/