Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Bootstrap-Based Fault Identification Method

붓스트랩을 활용한 이상원인변수의 탐지 기법

  • Kang, Ji-Hoon (School of Industrial Management Engineering, Korea University) ;
  • Kim, Seoung-Bum (School of Industrial Management Engineering, Korea University)
  • 강지훈 (고려대학교 산업경영공학부) ;
  • 김성범 (고려대학교 산업경영공학부)
  • Received : 2011.02.09
  • Accepted : 2011.04.28
  • Published : 2011.06.30
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Abstract

Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based $T^2$ decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric $T^2$ decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.

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References

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