| 基于支持向量机的故障模式识别研究 |
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| 引用本文： | 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(1):9-12. |
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| 作者姓名： | 齐保林 李凌均 李志农 |
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| 作者单位： | 1. 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
2. 郑州大学,振动工程研究所,河南,郑州,450001 |
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| 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.
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| 关 键 词： | 支持向量机(SVM) 模式识别 故障诊断 |
| 文章编号： | 1671-6833(2007)01-0009-03 |
| 修稿时间： | 2006年7月21日 |
Research on Fault Pattern Recognition Based on Support Vector Machines |
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| QI Bao-lin,LI Ling-jun,LI Zhi-nong.Research on Fault Pattern Recognition Based on Support Vector Machines[J].Journal of Zhengzhou University: Eng Sci,2007,28(1):9-12. |
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| Authors: | QI Bao-lin LI Ling-jun LI Zhi-nong |
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| Abstract: | SVM provides people with a completely new means of artificial intelligent diagnosis which was restricted by shortage of large quantity of fault datasets.In this paper,feature vectors extracted from vibration signals of rolling bearing were selected as inputs of support vector machines to recognize fault patterns of rolling bearing.The results show that SVMs have excellent classification performance to fault patterns of rolling bearing,even under noises and interference. |
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| Keywords: | support vector machines(SVMs) pattern recognition fault diagnosis |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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