| 基于支持向量机的故障诊断方法研究 |
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| 引用本文： | 齐保林,李凌均. 基于支持向量机的故障诊断方法研究[J]. 煤矿机械, 2007, 28(1): 182-184 |
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| 作者姓名： | 齐保林 李凌均 |
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| 作者单位： | 1. 郑州大学,振动工程研究所,郑州,450002;郑州牧业工程高等专科学校,郑州,450011
2. 郑州大学,振动工程研究所,郑州,450002 |
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| 摘 要： | 故障样本缺乏是制约智能故障诊断发展的重要原因。支持向量机是近10 a来提出的一种基于小样本的统计学习方法。将支持向量机分类算法用于滚动轴承的多类故障分类并与RBF神经网络进行对比研究。实验表明,在有限样本条件下,支持向量机算法比RBF神经网络具有更好的分类性能。
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| 关 键 词： | 支持向量机(SVM) 多类故障分类 人工神经网络 智能故障诊断 |
| 文章编号： | 1003-0794（2007）01-0182-03 |
| 修稿时间： | 2006-09-23 |
Research on Diagnosis Method of Mechanical Fault Based on Support Vector Machines |
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| QI Bao-lin,Li Ling-jun. Research on Diagnosis Method of Mechanical Fault Based on Support Vector Machines[J]. Coal Mine Machinery, 2007, 28(1): 182-184 |
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| Authors: | QI Bao-lin Li Ling-jun |
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| Affiliation: | 1. Research Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450G02, China; 2.Zhengzhou College of Animal Husbandry Engineering, Zhengzhou 450011, China |
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| Abstract: | The Shortage of fault samples is one of the main reasons that restrict the development of intelligent fault diagnosis.Support vector machine(SVM) is a statistic learning method based on less samples proposed in the last decade.In this paper,the classification algorithm of support vector machine is used to deal with the multi-class fault classification problem in intelligent fault diagnosis.The experimental results of trundle bearing fault diagnoses by using SVM is compared with that by using RBF neural network,which shows that the SVM method has higher classification performance than RBF neural network under the condition of restricted samples. |
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| Keywords: | support vector machines multi - class fault classification RBF neural network intelligent fault diagnosis |
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