Detection and identification of mean shift using independent component analysis in multivariate processes

Guler Z. O., BAKIR M. A.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.92, sa.9, ss.1920-1940, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 92 Sayı: 9
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1080/00949655.2021.2015352
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Metadex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1920-1940
  • Anahtar Kelimeler: Multivariate statistical process control, independent component analysis, shift detection, shift identification, control chart, DIAGNOSIS, PCA
  • Gazi Üniversitesi Adresli: Evet

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.