[1]张端金,汪爱娟.基于改进的小波核主元分析故障检测[J].郑州大学学报(工学版),2015,36(01):97-100.[doi:10.3969/ j. issn.1671 - 6833.2015.01.023]
　ZHANG Duan-jin,WANG Ai-juan.Fault Detection Based on Improved Wavelet Kernel Principal Component Analysis[J].Journal of Zhengzhou University (Engineering Science),2015,36(01):97-100.[doi:10.3969/ j. issn.1671 - 6833.2015.01.023]
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基于改进的小波核主元分析故障检测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
36
期数:
2015年01期
页码:
97-100
栏目:
出版日期:
2015-01-10

文章信息/Info

Title:
Fault Detection Based on Improved Wavelet Kernel Principal Component Analysis
作者:
张端金; 汪爱娟
郑州大学信息工程学院,河南郑州 450001
Author(s):
ZHANG Duan-jin; WANG Ai-juan
School of Information Engineering ,Zhengzhou University，Zhengzhou 450001 ，China
关键词:
核主元分析; 小波核函数; 小波去噪; 故障检测
Keywords:
kernel principal component analysis ;  wavelet kernel function;  wavelet denoising;  fault detection
分类号:
TP273
DOI:
10.3969/ j. issn.1671 - 6833.2015.01.023
文献标志码:
A
摘要:
研究了基于核主元分析的非线性系统故障检测问题.提出了一种改进的小波核主元分析的故障检测方法.该方法首先对数据进行小波去噪预处理,然后再利用小波核函数,将非线性的输入空间转换到线性特征空间.在特征空间使用主元分析,结合SPE统计量和T统计量对非线性系统进行故障检测.仿真结果表明:该方法能够提高故障检测性能．
Abstract:
The problem of fault detection for a class of nonlinear systems based on kernel principal componentanalysis is studied. The improved wavelet kernel principal component analysis is proposed. Firstly,the pro-posed method is applied to denose the data. Then,the preprocessed data is transformed by wavelet kernelfunction to map the nonlinear input space into linear characterization space. In the feature space,principalcomponent analysis is applied to detect faults for nonlinear system,in combination with SPE statistic and T’statistic. Simulation results show that the method can improve the fault detection performance.
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