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《Engineering Design》 2002-05
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Monitoring and fault diagnosis based on PCA for multivariable control system

CHEN Yong, LIANG Jun, LU Hao\;(Institute of System Engineering, Zhejiang University, Hangzhou 310027, China)  
Principal component analysis (PCA) is an effective method for process monitoring and fault diagnosis. PCA produces a compressed statistical model that gives linear combinations of the original variables that describe the major trends in a data set, and produces new variables that are uncorrelated with each other and are linear combinations of the original variables. The experiments results show that PCA is an efficient method to monitor performance of the process, and can detect faults resulted in change of product quality exactly.
【Fund】: 国家"863"计划资助项目(863-511-920-011).
【CateGory Index】: TP277
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5 Zhang Lei1),Gu Yongwei1,2),Gui Qingming1)and Ma Chaozhong1)1)Institute of Science,Information Engineering University,Zhengzhou 4500012)Institute of Surveying and Mapping,Information Engineering University,Zhengzhou 450052;BIASED ESTIMATOR BASED ON DIAGNOSIS AND MEASURE OF MULTICOLLINEARITY[J];Journal of Geodesy and Geodynamics;2007-02
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10 Cheng LongshengWu Kefa Dong Tianxin (Nanjing University of Sci. & Tech.)(Xian Jiaotong University);Large Sample Properties of Parameter Estimators for a Multivariate Linear Ultrastructural Relationship Model[J];CHINESE JOURNAL OF ENGINEERING MATHEMATICS;1996-04
【Secondary References】
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1 Zou Xiaoxin,Tian Liang,Liu Jizhen,Liu Hengtao(School of Control Science and Engineering,North China Electric Power University,Baoding 071003,China);Analysis on Rejection Combustion Disturbance Capacity of Steam Temperature System under Primary Frequency Modulation[J];Electric Power Science and Engineering;2008-01
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