Incorporating principal component analysis ...
Document type :
Article dans une revue scientifique: Article original
Permalink :
Title :
Incorporating principal component analysis into Hotelling T2 control chart for compositional data monitoring
Author(s) :
Imran, M. [Auteur]
Guangzhou University
Dai, H. L. [Auteur]
Guangzhou University
Zaidi, F. S. [Auteur]
Guangzhou University
Tran, Kim-Phuc [Auteur]
Dong-A University
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Abbas, Z. [Auteur]
East China Normal University [Shangaï] [ECNU]
Nazir, H. Z. [Auteur]
University of Sargodha
Guangzhou University
Dai, H. L. [Auteur]
Guangzhou University
Zaidi, F. S. [Auteur]
Guangzhou University
Tran, Kim-Phuc [Auteur]
Dong-A University
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Abbas, Z. [Auteur]
East China Normal University [Shangaï] [ECNU]
Nazir, H. Z. [Auteur]
University of Sargodha
Journal title :
Comput. Ind. Eng.
Abbreviated title :
Comput. Ind. Eng.
Volume number :
186
Pages :
109755
Publication date :
2024-06-29
ISSN :
0360-8352
English keyword(s) :
PCA
Statistical process monitoring
5-part CoDa
Statistical process monitoring
5-part CoDa
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
In the manufacturing industry, compositional data (CoDa) is a vital quality characteristic to be monitored. The proposed study has introduced a Hotelling control chart using principal component analysis to monitor CoDa ...
Show more >In the manufacturing industry, compositional data (CoDa) is a vital quality characteristic to be monitored. The proposed study has introduced a Hotelling control chart using principal component analysis to monitor CoDa explicitly. The proposed method overcomes the limitations of previous approaches by utilizing isometric log-ratio transformation to map the CoDa into unrestricted real space. A Monte Carlo simulation procedure is employed to evaluate the performance of the proposed multivariate control chart. The simulation involves varying combinations of the number of variables () and subgroup sizes to obtain metrics such as the average run length when the process is out-of-control (OOC) and the upper control limit. The article presents a comprehensive analysis of the proposed control chart’s statistical performance. These visual aids facilitate a deeper understanding of the method’s effectiveness in detecting OOC conditions in CoDa. Furthermore, two illustrative examples using synchronous machines and components of electronic scrap are provided to illustrate the practical implementation and effectiveness of the Hotelling -based principal component control chart for -part and -part CoDa in real-world scenarios.Show less >
Show more >In the manufacturing industry, compositional data (CoDa) is a vital quality characteristic to be monitored. The proposed study has introduced a Hotelling control chart using principal component analysis to monitor CoDa explicitly. The proposed method overcomes the limitations of previous approaches by utilizing isometric log-ratio transformation to map the CoDa into unrestricted real space. A Monte Carlo simulation procedure is employed to evaluate the performance of the proposed multivariate control chart. The simulation involves varying combinations of the number of variables () and subgroup sizes to obtain metrics such as the average run length when the process is out-of-control (OOC) and the upper control limit. The article presents a comprehensive analysis of the proposed control chart’s statistical performance. These visual aids facilitate a deeper understanding of the method’s effectiveness in detecting OOC conditions in CoDa. Furthermore, two illustrative examples using synchronous machines and components of electronic scrap are provided to illustrate the practical implementation and effectiveness of the Hotelling -based principal component control chart for -part and -part CoDa in real-world scenarios.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
ENSAIT
Junia HEI
ENSAIT
Junia HEI
Collections :
Submission date :
2024-07-16T21:20:22Z
2024-09-20T12:10:58Z
2024-09-20T12:10:58Z