False alarm classification for multivariate manufacturing processes of thin film transistor-liquid crystal displays

Ji Hoon Kang, Seoung Bum Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)


Control charts have been widely used to improve manufacturing processes by reducing variations and defects. In particular, multivariate control charts have been effectively applied with monitoring processes that contain many correlated variables. Most existing multivariate control charts are vulnerable to misclassification errors that originate because of the hypothesis tests. In particular, these often cause the generation of a large number of false alarms. In this paper, we propose a procedure to reduce false alarms by combining a multivariate control chart and data mining algorithms. Simulation and real case studies demonstrate that the proposed method effectively reduces the false alarm rate.

Original languageEnglish
Pages (from-to)21-29
Number of pages9
JournalJournal of Process Control
Publication statusPublished - 2015 Nov 10



  • Classification algorithm
  • False alarms
  • Multivariate control charts
  • TFT-LCD manufacturing process

ASJC Scopus subject areas

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

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