Multivariate Statistical Process Monitoring and Fault Diagnosis Based on an Integration Method of PCA-ICA and CSM

Ying Hua Yang; Yong Lu Chen; Xiao Bo Chen; Shu Kai Qin

doi:10.4028/www.scientific.net/AMM.84-85.110

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 84-85Multivariate Statistical Process Monitoring and...

Multivariate Statistical Process Monitoring and Fault Diagnosis Based on an Integration Method of PCA-ICA and CSM

Abstract:

In this paper, an approach for multivariate statistical process monitoring ans fault diagnosis based on an improved independent component analysis (ICA) and continuous string matching (CSM) is presented, which can detect and diagnose process fault faster and with higher confidence level. The trial on the Tennessee Eastman process demonstrates that the proposed method can diagnose the fault effectively. Comparison of the method with the well established principal component analysis is also made.

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Periodical:

Applied Mechanics and Materials (Volumes 84-85)

Pages:

110-114

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.84-85.110

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Online since:

August 2011

Authors:

Ying Hua Yang, Yong Lu Chen, Xiao Bo Chen, Shu Kai Qin

Keywords:

Continuous String Matching, Fault Diagnosis, Independent Component Analysis (ICA), Principal Component Analysis (PCA)

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