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

邓晓刚; 田学民

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

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

过程系统工程与过程安全 | Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2013年21卷第2期页码：163-1701004-954160454-1
- Affiliations：
  
  College of Information and Control Engineering, China University of Petroleum,Qingdao,China,266580
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(13)60454-1
  中图分类号：
- 纸质出版：2013
- Accepted：
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邓晓刚, 田学民. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection[J]. 中国化学工程学报（英文版）, 2013,21(2):163-1701004-954160454-1.

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.
邓晓刚, 田学民. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection[J]. 中国化学工程学报（英文版）, 2013,21(2):163-1701004-954160454-1. DOI： 10.1016/S1004-9541(13)60454-1.

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. DOI： 10.1016/S1004-9541(13)60454-1.

摘要

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.

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