Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

doi:10.1016/S1004-9541(13)60454-1

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (2): 163-170.DOI: 10.1016/S1004-9541(13)60454-1

• 过程系统工程与过程安全 • 上一篇下一篇

Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

邓晓刚, 田学民

College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China

收稿日期:2012-05-07 修回日期:2012-07-16 出版日期:2013-02-28 发布日期:2013-03-13
通讯作者: DENG Xiaogang
基金资助:
Supported by the National Natural Science Foundation of China (61273160), the Natural Science Foundation of Shandong Province of China (ZR2011FM014) and the Fundamental Research Funds for the Central Universities (10CX04046A).

Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

DENG Xiaogang, TIAN Xuemin

College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China

Received:2012-05-07 Revised:2012-07-16 Online:2013-02-28 Published:2013-03-13

摘要/Abstract

摘要： Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

关键词: nonlinear locality preserving projection, kernel trick, sparse model, fault detection

Abstract: Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

Key words: nonlinear locality preserving projection, kernel trick, sparse model, fault detection

邓晓刚, 田学民. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection[J]. Chinese Journal of Chemical Engineering, 2013, 21(2): 163-170.

DENG Xiaogang, TIAN Xuemin. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection[J]. Chin.J.Chem.Eng., 2013, 21(2): 163-170.

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Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

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