| 基于KPCA和SVM的电梯故障诊断系统 |
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| 引用本文： | 黄水霞,张广明,邱春玲,黄凯.基于KPCA和SVM的电梯故障诊断系统[J].机械设计与制造,2010(1). |
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| 作者姓名： | 黄水霞 张广明 邱春玲 黄凯 |
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| 作者单位： | 1. 南京工业大学,自动化学院,南京,210009
2. 江苏省特种设备安全监督检验研究院,南京,210009 |
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| 基金项目： | 江苏省社会发展计划项目(BS2007083) |
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| 摘 要： | 针对电梯故障的非线性特性及故障特征不明显,传统方法分析后留下的显著成分不能反映这种非线性属性等问题,提出了核主元分析(KPCA)和支持向量机(SVM)相结合的故障诊断方法。利用KPCA在高维空间具有较强的特征选择能力和支持向量机具有较强的辨识率的特点,通过核主元分析法提取电梯故障特征,以达到降维作用,再利用支持向量机分类模型进行故障辨识。实验证明用此方法进行电梯的故障诊断具有更快更好的诊断效果。
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| 关 键 词： | KPCA SVM 故障诊断 电梯 故障辨识 |
The elevator fault diagnosis system based on KPCA and SVM |
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| HUANG Shui-xia,ZHANG Guang-ming,QIU Chun-ling,HUANG Kai.The elevator fault diagnosis system based on KPCA and SVM[J].Machinery Design & Manufacture,2010(1). |
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| Authors: | HUANG Shui-xia ZHANG Guang-ming QIU Chun-ling HUANG Kai |
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| Affiliation: | 1 Nanjing University of Technology/a>;Nanjing 210009/a>;China;2 Jiangsu Province Special Equipmentsafety Supervision Inspection Institute/a>;China |
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| Abstract: | Generally,the elevator failures are non-linear and invisible.Aimed to solve the problem that the traditional method can't reflect this quality of non-linear,propose the method using KPCA and SVM.KPCA has great ability in features selection and SVM has good ability of identification.First extract the elevator fault feature using KPCA in order to reduce the dimension of fault data samples and then use SVM to identify the elevator fault based on their characteristics.Experiments prove that it has a better and ... |
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| Keywords: | KPCA SVM Fault diagnosis Elevator Fault identify |
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