| 基于EMD和AR模型的滚动轴承故障SVM识别 |
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| 引用本文： | 张晨罡,郝伟,李志农,王丽雅. 基于EMD和AR模型的滚动轴承故障SVM识别[J]. 煤矿机械, 2007, 28(7): 183-186 |
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| 作者姓名： | 张晨罡 郝伟 李志农 王丽雅 |
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| 作者单位： | 郑州大学,振动工程研究所,郑州,450001 |
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| 基金项目： | 河南省自然科学基金 , 河南省教育厅自然科学基金 , 河南省高校杰出科研创新人才工程项目 |
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| 摘 要： | 将经验模态分解和自回归(AR)模型应用到滚动轴承的故障诊断中,该方法先把轴承振动信号分解成不同特征时间尺度的固有模态函数,从而把非平稳信号处理转化为平稳信号处理问题,然后选取表征轴承故障的IMF分量,并建立其AR模型,提取模型的参数输入到支持向量机中进行识别。实验结果表明,该方法是有效的。
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| 关 键 词： | AR模型 经验模态分解 支持向量机 滚动轴承 |
| 文章编号： | 1003-0794（2007）07-0183-04 |
| 修稿时间： | 2007-03-14 |
SVM Recognition Method Based on EMD and AR Model in Rolling Bearing Fault Diagnosis |
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| ZHANG Chen-gang,HAO Wei,LI Zhi-nong,WANG Li-ya. SVM Recognition Method Based on EMD and AR Model in Rolling Bearing Fault Diagnosis[J]. Coal Mine Machinery, 2007, 28(7): 183-186 |
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| Authors: | ZHANG Chen-gang HAO Wei LI Zhi-nong WANG Li-ya |
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| Affiliation: | Research Institute of Vibration Engineering,Zhengzhou University, Zhengzhou 450001 ,China |
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| Abstract: | Empirical mode decomposition(EMD) and AR model are applied to the fault diagnosis of rolling bearing.The methodology developed decomposes the signal in intrinsic oscillation modes first,to translate the non-stationary signals into stationary signals.Then the autoregressive(AR) model of the selected IMF is established,and the parameters were served as input parameter of SVM to identify fault patterns of rolling bearing.The experimental result shows that the proposed approach is effective. |
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| Keywords: | AR model empirical mode decomposition(EMD) support vector machine(SVM) rolling bearing |
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