Multivariate Statistical Process Monitoring Based on Principal Discriminative Component Analysis

2021 ◽  
Author(s):  
Shanzhi Li ◽  
Yang Chen ◽  
Chudong Tong
Keyword(s):  
Process Monitoring ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Multivariate Statistical Process Monitoring ◽  
Statistical Process Monitoring

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

2011 ◽  
Vol 84-85 ◽  
pp. 110-114 ◽  
Author(s):  
Ying Hua Yang ◽  
Yong Lu Chen ◽  
Xiao Bo Chen ◽  
Shu Kai Qin
Keyword(s):  
Fault Diagnosis ◽  
Process Monitoring ◽  
Integration Method ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Tennessee Eastman Process ◽  
Multivariate Statistical Process Monitoring ◽  
Statistical Process Monitoring

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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