An EWMA-based process mean estimator with dynamic tuning capability

Abstract In this paper, we focus on the Exponentially Weighted Moving Average (EWMA) process mean estimator and its application to process adjustment. A novel dynamic-tuning EWMA estimator is proposed that has the capability of adjusting the control parameter dynamically in response to the underlying process random shifts. The current run's process mean is estimated using the EWMA equation and the newly adjusted control parameter. It is shown that the proposed estimator is very easy to implement and effective under many disturbance situations. Both industrial field data and Monte Carlo simulations are used to validate its performance.

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