Selection of number of principal components based on fault diagnosing performance optimization

Huagong Xuebao/Journal of Chemical Industry and Engineering (China)

Volumn 55, Issue 2, 2004, Pages 214-219

  Wang, Haiqing ^a   Yu, Shiming ^b  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Critical fault magnitude; Fault detection and isolation; Principal component analysis

Indexed keywords

CHEMICAL ENGINEERING; ERROR DETECTION; OPTIMIZATION;

CRITICAL FAULT MAGNITUDE; FAULT DETECTION AND ISOLATION; FAULT DIAGNOSING PERFORMANCE OPTIMIZATION; OPTIMAL CRITICAL FAULT MAGNITUDE;

PRINCIPAL COMPONENT ANALYSIS;

EID: 2642543525     PISSN: 04381157     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (6)

1. Zhang Jie, Yang Xianhui. Multivariate Statistical Process Control. Beijing: Chemical Industry Press, 2000.
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3. Wang Haiqing, Song Zhihuan, Li Ping. Improved PCA with Application to Process Monitoring and Fault Diagnosis. Journal of Chemical Industry and Engineering (China), 2001, 52: 471-475.
4. Valle S, Li W, Qin S J. Selection of the Number of Principal Components: the Variance of the Reconstruction Error Criterion with a Comparison to other Methods. Ind. Eng. Chem. Res., 1999, 38: 4389-4401.
5. Dunia R, Qin S J. Subspace Approach to Multidimensional Fault Identification and Reconstruction. AIChE J., 1998, 44: 1813-1831.
6. Jackson J E. A User's Guide to Principal Components. New York: Wiley, 1991.