Improved Kernel Principal Component Analysis and Its Application for Fault Detection

机译：改进的核主成分分析及其在故障检测中的应用

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The kernel principal component analysis (KPCA) based on feature vector selection (FVS) is proposed in this paper for fault detection in nonlinear system. Firstly, the KPCA algorithm is described in detail. Secondly, a feature vector selection (FVS) scheme based on a geometric consideration is adopted to reduce the computational cost of KPCA. Finally, the KPCA and KPCA based on FVS (FVS-KPCA) are applied to a simple nonlinear system. The fault detection results and the comparison confirm the superiority of FVS-KPCA in fault detection.

机译：提出了基于特征向量选择（FVS）的核主成分分析（KPCA），用于非线性系统的故障检测。首先，详细描述KPCA算法。其次，采用基于几何考虑的特征向量选择（FVS）方案来降低KPCA的计算成本。最后，将基于FVS的KPCA和KPCA（FVS-KPCA）应用于简单的非线性系统。故障检测结果和比较结果证实了FVS-KPCA在故障检测中的优越性。

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《International Conference on Intelligent Computing;ICIC 2008》|2008年|P.688-695|共8页
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Chuyao Chen; Daqi Zhu; Qian Liu;
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正文语种
中图分类人工智能理论;
关键词
kernel principal component analysis (kpca); feature vector selection (fvs); fault detection;

机译：内核主成分分析（kpca）;特征向量选择（fvs）;故障检测;

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4. Improved Kernel Principal Component Analysis and Its Application for Fault Detection [C] . Chuyao Chen, Daqi Zhu, Qian Liu Interntional Conference on Intelligent Computing . 2008

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Improved Kernel Principal Component Analysis and Its Application for Fault Detection

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