Data mining model-based control charts for multivariate and autocorrelated processes

Seoung Bum Kim, Weerawat Jitpitaklert, Sun Kyoung Park, Seung June Hwang

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional statistical process control (SPC) tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of multivariate and autocorrelated data found in modern systems. As the limitations of SPC methodology become increasingly obvious in the face of ever more complex processes, data mining algorithms, because of their proven capabilities to effectively analyze and manage large amounts of data, have the potential to resolve the challenging problems that are stretching SPC to its limits. In the present study we attempted to integrate state-of-the-art data mining algorithms with SPC techniques to achieve efficient monitoring in multivariate and autocorrelated processes. The data mining algorithms include artificial neural networks, support vector regression, and multivariate adaptive regression splines. The residuals of data mining models were utilized to construct multivariate cumulative sum control charts to monitor the process mean. Simulation results from various scenarios indicated that data mining model-based control charts performs better than traditional time-series model-based control charts.

Original languageEnglish
Pages (from-to)2073-2081
Number of pages9
JournalExpert Systems With Applications
Issue number2
Publication statusPublished - 2012 Feb 1


  • Autocorrelated process
  • Data mining
  • Model-based control chart
  • Multivariate process
  • Statistical process control

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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