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A density-based statistical process control scheme for high-dimensional and mixed-type observations

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  • Xianghui Ning
  • Fugee Tsung

Abstract

Statistical Process Control (SPC) techniques are useful tools for detecting changes in process variables. The structure of process variables has become increasingly complex as a result of increasingly complex technologies. The number of variables is usually large and categorical variables may appear alongside continuous variables. Such observations are considered to be high-dimensional and mixed-type observations. Conventional SPC techniques may lose their accuracy and efficiency in detecting changes in a process with high-dimensional and mixed-type observations. This article presents a density-based SPC approach, which is derived from a Local Outlier Factor (LOF) scheme, as a solution to this problem. The parameters in an LOF scheme are investigated and a procedure to design a corresponding control chart is presented. The good performance of the proposed control scheme is demonstrated via numerical simulation.

Suggested Citation

  • Xianghui Ning & Fugee Tsung, 2012. "A density-based statistical process control scheme for high-dimensional and mixed-type observations," IISE Transactions, Taylor & Francis Journals, vol. 44(4), pages 301-311.
    • Handle: RePEc:taf:uiiexx:v:44:y:2012:i:4:p:301-311
    DOI: 10.1080/0740817X.2011.587863
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