| 基于支持向量机的液压泵故障诊断 |
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| 引用本文： | 张国新,汤青波,许德昌.基于支持向量机的液压泵故障诊断[J].煤矿机械,2007,28(8):193-195. |
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| 作者姓名： | 张国新 汤青波 许德昌 |
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| 作者单位： | 江西理工大学,机电工程学院,江西,赣州,341000 |
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| 摘 要： | 支持向量机在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势,用ν-支持向量机构造"一对一"多分类算法,应用于ZB40液压泵的故障诊断,取得了较好效果,较神经网络方法,它不必预先提取信号的特征量,只需要少量的故障样本训练分类器,实用性好。
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| 关 键 词： | 支持向量机 液压泵 故障诊断 |
| 文章编号： | 1003-0794（2007）08-0193-03 |
| 修稿时间： | 2007-04-07 |
Study on Fault Diagnosis Based on SVM for Hydraulic Pump |
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| ZHANG Guo-xin,TANG Qing-bo,XU De-chang.Study on Fault Diagnosis Based on SVM for Hydraulic Pump[J].Coal Mine Machinery,2007,28(8):193-195. |
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| Authors: | ZHANG Guo-xin TANG Qing-bo XU De-chang |
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| Affiliation: | Faculty of Mechanical and Electronical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China |
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| Abstract: | Support vector machine(SVM)is effective to solve problems of pattern recognition under the condition of finite samples and high dimensional space.Instead of common c-SVM,ν-SVM was selected as binary classifier to construct multi-class SVMs,in which the meaning of parameter ν was more obvious and could be determined more easily.As an application example,4 kinds of real fault samples for ZB40 hydraulic pump were classified correctly using this algorithm. |
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| Keywords: | support vector machine hydraulic pump fault diagnosis |
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