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Adaptive nonparametric control chart for time-varying and multimodal processes

Authors
Kang, Ji HoonYu, JaehongKim, Seoung Bum
Issue Date
Jan-2016
Publisher
ELSEVIER SCI LTD
Keywords
Clustering; Data mining algorithm; Multivariate control chart; Multimodality; Time-varying process; False alarms
Citation
JOURNAL OF PROCESS CONTROL, v.37, pp.34 - 45
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF PROCESS CONTROL
Volume
37
Start Page
34
End Page
45
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/90053
DOI
10.1016/j.jprocont.2015.11.005
ISSN
0959-1524
Abstract
Multivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T-2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.
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