Detection and identification of mean shift using independent component analysis in multivariate processes
Yükleniyor...
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Ltd.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Multivariate Statistical Process Control (MSPC) methods are used commonly to detect and identify shifts in multivariate industrial processes. However, these methods are limited by assumptions and complexities. In this study, a new two-stage MSPC approach based on independent component analysis is proposed. The proposed method aims to provide solutions to both the determination and the identification of the shift in the mean vector of a multivariate process. In the first step of this new method, independent components extracted by ICA were used as monitoring statistics in the MSPC chart to detect the shift in the process. The second step of the method started to deal with the problem of identifying the source of this shift by decomposing the monitoring statistics. The simulation results show the superiority of the new method over traditional methods in both determining and identifying the shift in the process mean vector.
Açıklama
WOS:000731224700001
Anahtar Kelimeler
Multivariate Statistical Process Control, Independent Component Analysis, Shift Detection, Shift İdentification, Control Char
Kaynak
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Güler, Z. Ö., & Bakır, M. A., (2021): Detection and identification of mean shift using independent component analysis in multivariate processes, Journal of Statistical Computation and Simulation, doi: 10.1080/00949655.2021.2015352
