基于支持向量机MPLS的间歇过程故障诊断方法

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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

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-12-28 发布日期:2006-12-28
通讯作者: 李运锋

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

摘要/Abstract

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

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

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.

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

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基于支持向量机MPLS的间歇过程故障诊断方法

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

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