基于Fisher子空间特征提取的间歇过程监控和故障诊断

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基于Fisher子空间特征提取的间歇过程监控和故障诊断

赵旭; 阎威武; 邵惠鹤

Institute of Automation, Shanghai Jiao Tong University, Shanghai 200030, 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: 赵旭

Monitoring and fault diagnosis for batch process based on feature extract in Fisher subspace

ZHAO Xu; YAN Weiwu; SHAO Huihe

Institute of Automation, Shanghai Jiao Tong University, Shanghai 200030, China

Received:1900-01-01 Revised:1900-01-01 Online:2006-12-28 Published:2006-12-28
Contact: ZHAO Xu

摘要/Abstract

摘要： Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

关键词: batch monitoring;fault diagnosis;feature extract;Fisher discriminant analysis;penicillin fermentation process

Abstract: Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calculated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process variables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of penicillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

Key words: batch monitoring, fault diagnosis, feature extract, Fisher discriminant analysis, penicillin fermentation process

赵旭, 阎威武, 邵惠鹤. 基于Fisher子空间特征提取的间歇过程监控和故障诊断[J]. CIESC Journal.

ZHAO ,Xu, YAN ,Weiwu, SHAO ,Huihe. Monitoring and fault diagnosis for batch process based on feature extract in Fisher subspace[J]. .

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基于Fisher子空间特征提取的间歇过程监控和故障诊断

Monitoring and fault diagnosis for batch process based on feature extract in Fisher subspace

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