A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

›› 2009, Vol. 17 ›› Issue (3): 427-436.

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

ZHANG Xi^1,2, MA Sile³, YAN Weiwu², ZHAO Xu², SHAO Huihe²

1. Guangdong Electric Power Research Institute, Guangzhou 510600, China;
2. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
3. School of Control Science and Engineering, Shandong University, Jinan 250061, China

Received:2008-01-27 Revised:2008-11-05 Online:2009-06-28 Published:2009-06-28
Supported by:
Supported by the National Natural Science Foundation of China (60504033);the Open Project of State Key Laboratory of Industrial Control Technology in Zhejiang University (0708004)

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

张曦^1,2, 马思乐³, 阎威武², 赵旭², 邵惠鹤²

1. Guangdong Electric Power Research Institute, Guangzhou 510600, China;
2. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
3. School of Control Science and Engineering, Shandong University, Jinan 250061, China

通讯作者: MA Sile,E-mail:masile@sdu.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (60504033);the Open Project of State Key Laboratory of Industrial Control Technology in Zhejiang University (0708004)

Abstract

Abstract: A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. If faults have occurred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.

Key words: quality monitoring, quality prediction, Fisher discriminant analysis, kernel regression, fluid catalytic cracking unit

摘要： A novel systematic quality monitoring and prediction method based on Fisher discriminant analysis (FDA) and kernel regression is proposed. The FDA method is first used for quality monitoring. If the process is under normal condition, then kernel regression is further used for quality prediction and estimation. If faults have occurred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can effectively detect the fault and has better ability to predict the response variables than principle component regression (PCR) and partial least squares (PLS). Application results to the industrial fluid catalytic cracking unit (FCCU) show the effectiveness of the proposed method.

关键词: quality monitoring, quality prediction, Fisher discriminant analysis, kernel regression, fluid catalytic cracking unit

ZHANG Xi, MA Sile, YAN Weiwu, ZHAO Xu, SHAO Huihe. A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression[J]. , 2009, 17(3): 427-436.

张曦, 马思乐, 阎威武, 赵旭, 邵惠鹤. A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression[J]. , 2009, 17(3): 427-436.

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A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression

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