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

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

Chin.J.Chem.Eng. ›› 2014, Vol. 22 ›› Issue (6): 657-663.DOI: 10.1016/S1004-9541(14)60088-4

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

WANG Li¹, SHI Hongbo ²

1. School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 200230, China;
2. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2013-06-10 Revised:2013-08-26 Online:2014-06-06 Published:2014-06-28
Supported by:
Supported by the Special Scientific Research of Selection and Cultivation of Excellent Young Teachers in Shanghai Universities (YYY11076).

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

王丽¹, 侍洪波²

1. School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 200230, China;
2. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

通讯作者: Ali Akbar Amooey
基金资助:
Supported by the Special Scientific Research of Selection and Cultivation of Excellent Young Teachers in Shanghai Universities (YYY11076).

Abstract

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.

Key words: nonlinear process, fault detection, kernel partial least squares, statistical local approach

摘要： 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.

关键词: nonlinear process, fault detection, kernel partial least squares, statistical local approach

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]. Chinese Journal of Chemical Engineering, 2014, 22(6): 657-663.

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

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

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