Unsupervised Fault Detection for Refrigeration Showcase Systems with Kernel Principal Component Analysis based Multivariate Statistical Process Control using Feature Selection with Maximal Information Coefficient

2021 ◽  
Vol 141 (4) ◽  
pp. 345-353
Author(s):  
Kiyo Arai ◽  
Yoshikazu Fukuyama ◽  
Kenya Murakami ◽  
Tetsuro Matsui
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Statistical Process Control ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Information Coefficient ◽  
Maximal Information Coefficient

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.

scholarly journals Fault Analysis Of Process System Using Multi Block Principal Component Analysis

REAKTOR ◽  
2017 ◽  
Vol 7 (02) ◽  
pp. 61
Author(s):  
S. B. Sasongko ◽  
K. A. Ibrahim ◽  
A. Ahmad
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Analysis ◽  
Multivariate Statistical ◽  
Statistical Process

This research looks into the issues of the quality improvement based on process control instead of product control using multivariate statistical process contro. A deterministic model of a proton exchange membrane fuel cell (PEM-FC) power plant was used as a case study to represent a multi variable or mukti equipment system. A three-step approach is proposed which  can be classified into fault detection, fault isolation, and faulr diagnosis. The fault detection and the isolation utilize the multivariate analysis and yhe contro chart method , which uses the series multi-block principal component analysis  of extended of PCA method. The series block principal component abalysis is solved using the non linear iteration partial least squares (NIPALS) algorithm. The SB-PCA can advangeouly incorporate the control chart, namely, T2 Hotelling control chart. In the fault diagnosis chart, the normalized variable method was successfully applied in this study with promising results. As a conclution, the result of this study demonstrated the potentials of multivariate statistical process control in solving fault detection and diagnosis problem for multi variable and multi equipment system.Keywords : statistical process control, principal component, fault analysis

scholarly journals Investigation of Multivariate Statistical Process Control in R Enviroment

2017 ◽  
Vol 11 (2) ◽  
pp. 36-40
Author(s):  
József Mihalkó ◽  
Róbert Rajkó
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Principal Component ◽  
Theoretical Knowledge ◽  
Multivariate Statistical Process Control ◽  
Simulation Studies ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
R Project ◽  
Practical Model

At the first stage of our work, the theoretical knowledge needed to use the multivariate statistical process control (MSPC) was explored. Last year, we clarified the sometimes confused concepts, equations, and formulas [1]. At the se­cond stage, R project simulation studies and some food industrial practical model investigations are carried out for con­firming the MSPC advantages compared with the univariate ones. Furthermore, we analyse, using principal component analysis (PCA), what could cause the outlying values. Moreover, we will demonstrate how to use the MYT-decomposition.

Application of multivariate statistical process control to batch operations

2000 ◽  
Vol 24 (2-7) ◽  
pp. 291-296 ◽  
Author(s):  
B. Lennox ◽  
H.G. Hiden ◽  
G.A. Montague ◽  
G. Kornfeld ◽  
P.R. Goulding
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process
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