Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (1): 144-153.doi: 10.21629/JSEE.2019.01.14

• Systems Engineering • Previous Articles     Next Articles

Variable selection-based SPC procedures for high-dimensional multistage processes

Sangahn KIM*()   

  • Received:2017-05-26 Online:2019-02-27 Published:2019-02-27
  • Contact: Sangahn KIM E-mail:skim@siena.edu
  • About author:KIM Sangahn received his Ph.D. degree in Department of Industrial and Systems Engineering, Rutgers University, New Jersey, USA. He is an assistant professor in Department of Business Analytics and Acturial Science, Siena College, New York, USA. He is a recipient of the Richard A. Freund International Scolarship by the American Society of Quality (ASQ) in 2016. He also won the Tayfur Altiok Memaorial Scholarship and the Best Ph.D. Student Award in 2017 by Rutgers University. His research interests include statistical process modeling and monitoring, data mining and stochastic process. E-mail:skim@siena.edu
  • Supported by:
    the Qatar National Research Fund(NPRP 5-364-2-142);the Qatar National Research Fund(NPRP 7-1040-2-293);This work was supported by the Qatar National Research Fund (NPRP 5-364-2-142; NPRP 7-1040-2-293)

Abstract:

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring (SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average (MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length (ARL) for the detection of process mean shift.

Key words: diagnosis procedure, deviance residual, fault identification, model-based control chart, multistage process monitoring, variable selection