| 基于离散粒子群和支持向量机的故障诊断方法 |
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| 引用本文： | 王灵,俞金寿.基于离散粒子群和支持向量机的故障诊断方法[J].华东理工大学学报(自然科学版),2005,31(5):697-700. |
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| 作者姓名： | 王灵 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;华东理工大学自动化研究所,上海,200237 |
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| 摘 要： | 针对与故障不相关的变量会影响分类器性能，从而导致故障诊断正确率下降，提出一种将离散粒子群算法（PSO）与支持向量机（SVM）相结合寻找故障特征变量的优化算法。该算法实现了数据降维和故障特征保留，有效地提高了故障诊断性能。基于连续搅拌釜式反应器（CSTR）的仿真实例验证了该算法古白有诗性．
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| 关 键 词： | 故障诊断 粒子群算法(PSO) 支持向量机(SVM) 特征选择 CSTR |
| 文章编号： | 1006-3080（2005）05-0697-04 |
| 收稿时间： | 2004-09-09 |
| 修稿时间： | 2004年9月9日 |
Fault Diagnosis Based on Discrete Particle Swarm Optimization and Support Vector Machine |
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| WANG Ling,YU Jin-shou.Fault Diagnosis Based on Discrete Particle Swarm Optimization and Support Vector Machine[J].Journal of East China University of Science and Technology,2005,31(5):697-700. |
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| Authors: | WANG Ling YU Jin-shou |
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| Abstract: | To overcome the disadvantages that irrelevant variables spoil classifiers and decrease the correct classification rates of faults,a new optimization algorithm based on discrete particle swarm optimization(PSO) and support vector machines(SVM) is presented to directly search for fault feature variables.As it can reduce the dimensionality of data space and preserve the fault features,the algorithm greatly improve the performance of fault diagnosis.The simulation results of fault diagnosis on continuous stired-tank reactor(CSTR) prove its validity. |
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| Keywords: | fault diagnosis PSO SVM feature selection CSTR |
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