| 基于KPCA-SVC的复杂过程故障诊断 |
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| 引用本文： | 刘爱伦,袁小艳,俞金寿.基于KPCA-SVC的复杂过程故障诊断[J].仪器仪表学报,2007,28(5):870-874. |
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| 作者姓名： | 刘爱伦 袁小艳 俞金寿 |
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| 作者单位： | 华东理工大学自动化研究所,上海,200237 |
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| 摘 要： | 本文提出了一种将核主元分析方法与支持向量机分类相结合进行故障诊断的方法,运用该方法对连续搅拌釜式反应器（CSTR）进行实时的故障诊断,实验结果表明KPCA-SVC故障诊断方法既充分利用了KPCA的特征提取能力和SVC的良好的分类能力,又避免了复杂的计算,有利于提高故障诊断模型的实时性。
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| 关 键 词： | 核主元分析(KPCA) 支持向量机分类(SVC) 故障诊断 |
| 修稿时间： | 2006-06 |
Fault diagnosis of complex chemical process based on KPCA- SVC |
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| Liu Ailun,Yuan Xiaoyan,Yu Jinshou.Fault diagnosis of complex chemical process based on KPCA- SVC[J].Chinese Journal of Scientific Instrument,2007,28(5):870-874. |
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| Authors: | Liu Ailun Yuan Xiaoyan Yu Jinshou |
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| Affiliation: | Research Institution of Automation, East China University of Science and Technology, Shanghai 200237, China |
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| Abstract: | Support vector machine(SVM)is an effective fault diagnosis method,but a number of data may lead to a more complicated structure of SVM classifier.By integrating the characteristics of kernel principal component anal- ysis(KPCA)and SVM,a new fault diagnosis method is presented in this paper.The new method was applied to continuous stirred tank reactor(CSTR)model and the results show that this new method avoids complex computation and improves the real-time property of the fault diagnosis model. |
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| Keywords: | kernel principal component analysis(KPCA) support vector classification(SVC) fault diagnosis |
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