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

作     者：张曦 马思乐 阎威武 赵旭 邵惠鹤 ZHANG Xi;MA Sile;YAN Weiwu;ZHAO Xu;SHAO Huihe

作者机构：Guangdong Electric Power Research Institute Department of AutomationShanghai Jiao Tong University School of Control Science and EngineeringShandong University 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2009年第17卷第3期

页      面：427-436页

核心收录：

学科分类：0710[理学-生物学] 02[经济学] 0202[经济学-应用经济学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 020208[经济学-统计学] 07[理学] 0817[工学-化学工程与技术] 0703[理学-化学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：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) 

主　　题：quality monitori-ng -quality prediction Fisher discriminant analysis kernel regression fluid catalyticcracking 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 un-der normal condition, then kernel regression is further used for quality prediction and estimation. If faults have oc-curred, the contribution plot in the fault feature direction is used for fault diagnosis. The proposed method can ef-fectively 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.