Adaptive nonparametric control chart for time-varying and multimodal processes

Ji Hoon Kang, Jaehong Yu, Seoung Bum Kim

Research output: Contribution to journalArticle

9 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

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

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