[1]齐保林,李凌均,李志农..基于支持向量机的故障模式识别研究[J].郑州大学学报(工学版),2007,28(01):9-11,15.[doi:10.3969/j.issn.1671-6833.2007.01.003]
　Qi Baolin,LI Lingjun,Li Zhinong.Research on failure mode recognition based on support vector machine[J].Journal of Zhengzhou University (Engineering Science),2007,28(01):9-11,15.[doi:10.3969/j.issn.1671-6833.2007.01.003]

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基于支持向量机的故障模式识别研究()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

28

期数:

2007年01期

页码:

9-11,15

栏目:

出版日期:

1900-01-01

文章信息/Info

Title:

Research on failure mode recognition based on support vector machine

作者:

齐保林; 李凌均; 李志农.

郑州大学,振动工程研究所,河南,郑州,450001;郑州牧业工程高等专科学校,食品工程系,河南,郑州,450011, 郑州大学,振动工程研究所,河南,郑州,450001

Author(s):

Qi Baolin; LI Lingjun; Li Zhinong

关键词:

支持向量机(SVM);  模式识别;  故障诊断

Keywords:

Support Vector Machine (SVM);  pattern recognition;  Troubleshooting

DOI:

10.3969/j.issn.1671-6833.2007.01.003

文献标志码:

A

摘要:

支持向量机为因缺乏大量故障样本受到制约的智能诊断提供了一个全新的途径.从振动信号中提取特征向量作为支持向量机的输入,对滚动轴承故障模式进行识别.实验表明,在含噪声条件下支持向量机对滚动轴承故障模式仍然具有优秀的分类性能.

Abstract:

Support vector machines provide a new way for intelligent diagnosis that is constrained by the lack of a large number of fault samples. The feature vector is extracted from the vibration signal as the input of the support vector machine to identify the rolling bearing failure mode. Experiments show that the support vector machine still has excellent classification performance against rolling bearing failure modes under noisy conditions.

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