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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2013, Vol. 14 Issue (7): 520-534    DOI: 10.1631/jzus.A1300003
Chemical Engineering     
Statistical process monitoring based on improved principal component analysis and its application to chemical processes
Chu-dong Tong, Xue-feng Yan, Yu-xin Ma
Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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Abstract  In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling’s T2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

Key words： Fault detection      Principal component analysis (PCA)      Correlative principal components (CPCs)      Tennessee Eastman process     
Received: 02 January 2013      Published: 01 July 2013
CLC:  TQ086.3  
  TP277  
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Chu-dong Tong
Xue-feng Yan
Yu-xin Ma
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Chu-dong Tong, Xue-feng Yan, Yu-xin Ma. Statistical process monitoring based on improved principal component analysis and its application to chemical processes. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2013, 14(7): 520-534.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A1300003     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2013/V14/I7/520

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