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Monitoring Process Variability for Stationary Process Data

Published

Author(s)

Nien F. Zhang, Adam L. Pintar

Abstract

Processes that arise naturally, e.g., from manufacturing or the environment, often exhibit complicated autocorrelation structures. When monitoring such a process for changes in variance, accounting for that autocorrelation structure is critical. While charts for monitoring the variance of processes of independent observations and some specific autocorrelated processes have been proposed in the past, the chart presented in this article can handle any general stationary process, which is the major contribution of the work. The performance of the proposed chart was examined through simulations for AR(1) and ARMA(1,1) processes and demonstrated with examples.
Citation
Technometrics

Keywords

Autocorrelated processes, Average run length, Exponentially weighted mean square, Nonstationary processes, Statistical process control, Variance shift.

Citation

Zhang, N. and Pintar, A. (2014), Monitoring Process Variability for Stationary Process Data, Technometrics, [online], https://doi.org/10.1002/qre.1672 (Accessed May 24, 2024)

Issues

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Created June 20, 2014, Updated January 27, 2020