Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

王丽; 侍洪波

doi:10.1016/S1004-9541(14)60088-4

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Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

催化、动力学与反应工程 | Updated：2026-01-06

- Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes
- Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes
- 中国化学工程学报（英文版） 2014年22卷第6期页码：657-6631004-954160088-4
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(14)60088-4
  中图分类号：
- 纸质出版：2014
- Accepted：
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王丽, 侍洪波. Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes[J]. 中国化学工程学报（英文版）, 2014,22(6):657-6631004-954160088-4.

WANG Li, SHI Hongbo . Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes[J]. Chin.J.Chem.Eng., 2014, 22(6): 657-663.
王丽, 侍洪波. Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes[J]. 中国化学工程学报（英文版）, 2014,22(6):657-6631004-954160088-4. DOI： 10.1016/S1004-9541(14)60088-4.

WANG Li, SHI Hongbo . Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes[J]. Chin.J.Chem.Eng., 2014, 22(6): 657-663. DOI： 10.1016/S1004-9541(14)60088-4.

摘要

Abstract

In this paper

an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares (KPLS). By integrating the statistical local approach (SLA) into the KPLS framework

two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides

advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation

which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.

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