On-line fault detection using SVM-based dynamic MPLS for batch processes

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On-line fault detection using SVM-based dynamic MPLS for batch processes

LI Yunfeng^a; WANG Zhifeng^b; YUAN Jingqi^a,c

^a Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, China
^b Department of Automation, Shanghai Second Polytechnic University, Shanghai 201209, China
^c State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China

Received:1900-01-01 Revised:1900-01-01 Online:2006-12-28 Published:2006-12-28
Contact: LI Yunfeng

基于支持向量机MPLS的间歇过程故障诊断方法

李运锋^a; 汪志锋^b; 袁景淇^a,c

^aDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200030, China
^b Department of Automation, Shanghai Second Polytechnic University, Shanghai 201209, China
^c State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China

通讯作者: 李运锋

Abstract

Abstract: In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

Key words: fault detection, multiway partial least squares, support vector machines, time-lagged window

摘要： In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

关键词: fault detection;multiway partial least squares;support vector machines;time-lagged window

LIYunfeng,WANGZhifeng, YUANJingqi. On-line fault detection using SVM-based dynamic MPLS for batch processes[J]. .

李运锋,汪志锋, 袁景淇. 基于支持向量机MPLS的间歇过程故障诊断方法[J]. CIESC Journal.

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