Improved design of robust exponentially weighted moving average control charts for autocorrelated processes

Hyun Cheol Lee, Daniel W. Apley

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Residual-based control charts for autocorrelated processes are known to be sensitive to time series modeling errors, which can seriously inflate the false alarm rate. This paper presents a design approach for a residual-based exponentially weighted moving average (EWMA) chart that mitigates this problem by modifying the control limits based on the level of model uncertainty. Using a Bayesian analysis, we derive the approximate expected variance of the EWMA statistic, where the expectation is with respect to the posterior distribution of the unknown model parameters. The result is a relatively clean expression for the expected variance as a function of the estimated parameters and their covariance matrix. We use control limits proportional to the square root of the expected variance. We compare our approach to two other approaches for designing robust residual-based EWMA charts and argue that our approach generally results in a more appropriate widening of the control limits.

Original languageEnglish (US)
Pages (from-to)337-352
Number of pages16
JournalQuality and Reliability Engineering International
Issue number3
StatePublished - Apr 2011


  • autoregressive moving average models
  • exponentially weighted moving average
  • model uncertainty
  • residual-based control charts
  • robust design
  • time series

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


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