Abstract

There is a rich literature on methods for detecting out-of-control signals. However, the literature on methods for diagnosing out-of-control signals is not as rich; particularly, for methods that detect the specific levels and/or values of the covariates driving the change(s) in the process. In this paper, we propose a novel approach to this problem using a combination of data mining methods. We provide a variety of simulated scenarios with different out-of control signals and show how this method is able to isolate the specific values of the covariates contributing to the out-of-control signals. In addition, we compare the performance of our method to partial dependence plots (PPDs) and suggest how the strengths of the two can be combined in order to develop an even more robust scheme for diagnosing out-of-control signals.

Original languageEnglish (US)
Title of host publication62nd IIE Annual Conference and Expo 2012
PublisherInstitute of Industrial Engineers
Pages1506-1515
Number of pages10
StatePublished - 2012
Event62nd IIE Annual Conference and Expo 2012 - Orlando, FL, United States
Duration: May 19 2012May 23 2012

Other

Other62nd IIE Annual Conference and Expo 2012
CountryUnited States
CityOrlando, FL
Period5/19/125/23/12

Keywords

  • Anomaly detection
  • Partial dependence plots
  • Random forests
  • Rule-based classifiers
  • Statistical process control

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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  • Cite this

    Dávila, S., Torres-Burgos, M., & Runger, G. (2012). Rule-based methods for diagnosing out-of-control signals. In 62nd IIE Annual Conference and Expo 2012 (pp. 1506-1515). Institute of Industrial Engineers.