Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Dirk Lieftucht"'
Publikováno v:
2018 Global Smart Industry Conference (GloSIC).
Breakout is the most expensive and dangerous issue of continuous casting, which causes the loss of production time and significant yield penalties. The common cause of breakout is sticker, that is a part of strand shell, which adheres to a mold surfa
Publikováno v:
Anais do Seminário de Aciaria, Fundição e Metalurgia de Não-Ferrosos.
Autor:
Sebastian Engell, Dirk Lieftucht, Marten Völker, Uwe Kruger, Christian Sonntag, George W. Irwin
Publikováno v:
Control Engineering Practice. 17:478-493
Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and
Publikováno v:
IEE Proceedings - Control Theory and Applications. 153:437-446
Treasure et al. (2004) recently proposed a new subspace-monitoring technique, based on the N4SID algorithm, within the multivariate statistical process control framework. This dynamic-monitoring method requires considerably fewer variables to be anal
Publikováno v:
Industrial & Engineering Chemistry Research. 45:1677-1688
This paper presents the second part of the two-part analysis of statistical monitoring of complex multivariate processes. Part I introduced an effective method to remove both autocorrelation and cross-correlation from the monitored variables. This pa
Publikováno v:
Industrial & Engineering Chemistry Research. 45:1659-1676
The work summarized in this paper represents the first part of a two-paper analysis of statistical monitoring of complex dynamic multivariate processes. Motivated by recent research highlighting the difficulties of monitoring autocorrelated variables
Publikováno v:
Scopus-Elsevier
This paper analyses the potential of multivariate statistical process control in identifying and isolating process fault conditions. The analysis reveals that existing work suffers from inherent limitations if complex fault scenarios arise. Based on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b7c8b371e757e63f66a8ee9fa2f365f
https://doi.org/10.1016/b978-008044485-7/50068-3
https://doi.org/10.1016/b978-008044485-7/50068-3
Akademický článek
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Publikováno v:
Scopus-Elsevier
This paper analyses a variable reconstruction technique for identifying a faulty sensor. The reconstruction is associated with the application of principal component analysis (PCA) and attempts to remove "fault information" from the sensor reading. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e12582c451f9d3bee8b0c51a36e7449
http://www.scopus.com/inward/record.url?eid=2-s2.0-8744289580&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-8744289580&partnerID=MN8TOARS