Adaptive nonparametric control chart for time-varying and multimodal processes

Ji Hoon Kang, Jaehong Yu, Seoung Bum Kim

Research output: Contribution to journalArticle

7 Citations (Scopus)

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 T2 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.

Original languageEnglish
Pages (from-to)34-45
Number of pages12
JournalJournal of Process Control
Volume37
DOIs
Publication statusPublished - 2016 Jan 1

Fingerprint

Control Charts
Time-varying
Statistical process control
Thin film transistors
Liquid crystal displays
Defects
Multivariate Statistical Process Control
Multivariate Control Charts
Monitoring
Hotelling's T2
Thin-film Transistor
Liquid Crystal Display
Experiments
Manufacturing
Control charts
Demonstrate
Experiment

Keywords

  • Clustering
  • Data mining algorithm
  • False alarms
  • Multimodality
  • Multivariate control chart
  • Time-varying process

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Modelling and Simulation
  • Computer Science Applications

Cite this

Adaptive nonparametric control chart for time-varying and multimodal processes. / Kang, Ji Hoon; Yu, Jaehong; Kim, Seoung Bum.

In: Journal of Process Control, Vol. 37, 01.01.2016, p. 34-45.

Research output: Contribution to journalArticle

@article{ee97085abb214bb48481910b61c7b521,
title = "Adaptive nonparametric control chart for time-varying and multimodal processes",
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 T2 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.",
keywords = "Clustering, Data mining algorithm, False alarms, Multimodality, Multivariate control chart, Time-varying process",
author = "Kang, {Ji Hoon} and Jaehong Yu and Kim, {Seoung Bum}",
year = "2016",
month = "1",
day = "1",
doi = "10.1016/j.jprocont.2015.11.005",
language = "English",
volume = "37",
pages = "34--45",
journal = "Journal of Process Control",
issn = "0959-1524",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Adaptive nonparametric control chart for time-varying and multimodal processes

AU - Kang, Ji Hoon

AU - Yu, Jaehong

AU - Kim, Seoung Bum

PY - 2016/1/1

Y1 - 2016/1/1

N2 - 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 T2 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.

AB - 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 T2 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.

KW - Clustering

KW - Data mining algorithm

KW - False alarms

KW - Multimodality

KW - Multivariate control chart

KW - Time-varying process

UR - http://www.scopus.com/inward/record.url?scp=84948417168&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84948417168&partnerID=8YFLogxK

U2 - 10.1016/j.jprocont.2015.11.005

DO - 10.1016/j.jprocont.2015.11.005

M3 - Article

VL - 37

SP - 34

EP - 45

JO - Journal of Process Control

JF - Journal of Process Control

SN - 0959-1524

ER -