Adaptive nonparametric control chart for time-varying and multimodal processes
- Authors
- Kang, Ji Hoon; Yu, Jaehong; Kim, 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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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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