Interpretations of fault identification in multivariate manufacturing processes

Kyu Jong Lee, Ji Hoon Kang, Jae Hong Yu, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Multivariate control charts have been widely recognised as efficient tools for detection of abnormal behaviour in multivariate processes. However, these charts provide only limited information about the contribution of any specific variable to an out-of-control signal. To address this limitation, some fault identification methods have been developed to identify contributors to an abnormality. In real situations, however, a couple of tasks should be further considered with these contributors to improve their applicability and to facilitate interpretation of faults. This study presents a rank sum-based summarisation technique and a decision tree algorithm to facilitate the interpretation of fault identification results. Experimental results with real data from the manufacturing process for a thin-film transistor-liquid crystal display (TF-LCD) demonstrate the applicability and effectiveness of the proposed methods.

Original languageEnglish
Pages (from-to)395-408
Number of pages14
JournalEuropean Journal of Industrial Engineering
Issue number3
Publication statusPublished - 2015


  • Data mining
  • Decision tree
  • Fault identification
  • SPC
  • Statistical process control
  • T decomposition
  • TFT-LCD manufacturing process

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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