| 递归核PCA及其在非线性过程自适应监控中的应用 |
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| 引用本文： | 谢磊,王树青.递归核PCA及其在非线性过程自适应监控中的应用[J].化工学报,2007,58(7):1776-1782. |
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| 作者姓名： | 谢磊 王树青 |
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| 作者单位： | 浙江大学先进控制研究所，工业控制技术国家重点实验室 |
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| 基金项目： | 国家自然科学基金
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中国博士后科学基金 |
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| 摘 要： | PCA、PLS作为常用的多变量统计监控算法，一般适用于线性、定常的过程。针对实际工业过程的时变、非线性特性，提出了一种递归核PCA（RKPCA）方法用于非线性过程的自适应监控。RKPCA算法通过将递归奇异值分解推广到核空间，给出了核形式描述的递归KPCA算法，运算复杂度比KPCA明显降低，保证非线性监控模型能够在线更新。在Alstom工业燃气发生装置上的自适应监控表明，所提出的RKPCA算法能够及时跟踪非线性过程的时变特征，保证了监控模型的有效性。
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| 关 键 词： | 时变非线性过程 故障检测 递归核主元分析 自适应监控 递归 线性过程 自适应 监控模型 应用 nonlinear processes monitoring adaptive application kernel 有效性 时变特征 及时跟踪 发生装置 工业燃气 Alstom 在线更新 非线性 运算复杂度 KPCA |
| 文章编号： | 0438-1157（2007）07-1776-07 |
| 收稿时间： | 2006-8-7 |
| 修稿时间： | 2006-08-072007-03-09 |
Recursive kernel PCA and its application in adaptive monitoring of nonlinear processes |
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| XIE Lei,WANG Shuqing.Recursive kernel PCA and its application in adaptive monitoring of nonlinear processes[J].Journal of Chemical Industry and Engineering(China),2007,58(7):1776-1782. |
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| Authors: | XIE Lei WANG Shuqing |
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| Affiliation: | Institute of Advanced Process Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang , China |
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| Abstract: | As widely used process monitoring techniques,principal component analysis(PCA)and partial least squares(PLS)are limited to the application in linear and time-invariant systems.To handle the nonlinear and time-varying characteristics of real processes,a recursive kernel PCA(RKPCA)algorithm was proposed for adaptive monitoring of nonlinear processes.By extending the incremental singular value decomposition(SVD)to the kernel space,the kernel formulation of incremental kernel PCA,which possessed much lower computational complexity and was suitable for online model updating,was obtained.Finally,the proposed algorithm was applied to the Alstom gasifier for adaptive monitoring and RKPCA could efficiently capture the time-varying and nonlinear relationship in process variables. |
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| Keywords: | time-varying nonlinear process fault detection recursive kernel principal component analysis adaptive process monitoring |
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