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

张曦; 马思乐; 阎威武; 赵旭; 邵惠鹤

doi:10.1016/S1004-9541(08)60227-X

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

Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2009年17卷第3期页码：427-436
- Affiliations：
  
  1. Guangdong Electric Power Research Institute,Guangzhou,China,510600
  2. Department of Automation, Shanghai Jiao Tong University,Shanghai,China,200240
  3. School of Control Science and Engineering, Shandong University,Jinan,China,250061
- Author bio：
- Funds：
  
  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)
- DOI：10.1016/S1004-9541(08)60227-X
  中图分类号：
- 纸质出版：2009
- Accepted：
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张曦, 马思乐, 阎威武, 等. A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression[J]. 中国化学工程学报（英文版）, 2009,17(3):427-436.

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, 等. A Novel Systematic Method of Quality Monitoring and Prediction Based on FDA and Kernel Regression[J]. 中国化学工程学报（英文版）, 2009,17(3):427-436. DOI： 10.1016/S1004-9541(08)60227-X.

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. DOI： 10.1016/S1004-9541(08)60227-X.

摘要

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.

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