Control chart pattern recognition of manufacturing process using wavelet feature-based artificial neural networks

Jun Seok Kim, Sang Hoon Park, Cheong Sool Park, Hyo Heon Ko, Sung Shick Kim, Jun Geol Baek

Research output: Contribution to journalArticle

1 Citation (Scopus)


Control chart pattern recognition is one of the most important tools in the identification of process problems in modern manufacturing industries. Abnormal patterns including systematic, cyclic, drift, and shift could be involved with certain assignable causes. Conventional control charts could not inherently recognize these patterns. In this paper, multi-resolution wavelet analysis is used to extract features. A self-organizing map then generates cluster vectors with wavelet coefficients. Using these features, a back-propagation network classifies unnatural patterns. The performance evaluation result is better than those of other competitive methods.

Original languageEnglish
Pages (from-to)863-866
Number of pages4
JournalAdvanced Science Letters
Publication statusPublished - 2012 Jun 1



  • Artificial neural network
  • Control charts
  • Feature extraction
  • Multi-class classification
  • Pattern recognition
  • Wavelet analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Health(social science)
  • Mathematics(all)
  • Education
  • Environmental Science(all)
  • Engineering(all)
  • Energy(all)

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