MULTIVARIATE STATISTICAL PROCESS MONITORING USING MULTI-SCALE KERNEL PRINCIPAL COMPONENT ANALYSIS

2006 ◽  
Vol 39 (13) ◽  
pp. 108-113 ◽  
Author(s):  
Xiaogang Deng ◽  
Xuemin Tian
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Multi Scale ◽  
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.

Research on Nonlinear Process Monitoring and Fault Diagnosis Based on Kernel Principal Component Analysis

2009 ◽  
Vol 413-414 ◽  
pp. 583-590 ◽  
Author(s):  
Fei He ◽  
Min Li ◽  
Jian Hong Yang ◽  
Jin Wu Xu
Keyword(s):  
Principal Component Analysis ◽  
Production Process ◽  
Statistical Process Control ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process

In order to monitor nonlinear production process effectively, multivariate statistical process control based on kernel principal component analysis is applied to process monitoring and diagnosis. Squared prediction error (SPE) statistic of the kernel principal component analysis (KPCA) model is used for process monitoring, and the fault causes of the production process could be tracked by the methods of data reconstruction and the optimal neighbor selection strategy. Simulation data and Tennessee Eastman process data are used for model validation, as a result the proposed method has better performance on abnormality detecting, compared with multivariate statistical process control based on linear principal component analysis. What is more, the causes of the faults are tracked effectively, thus the production process can be adjusted to prevent substandard products.

Sign in / Sign up

Export Citation Format

Share Document