An adaptive exponentially weighted moving average control chart for monitoring process variances

Abstract

The exponentially weighted moving average (EWMA) control chart is efficient in detecting small changes in process parameters but less efficient when the changes are relatively large, due to what is known as the inertia problem. To diminish the inertia, an adaptive EWMA (AEWMA) chart has been proposed for monitoring process locations to improve over the traditional EWMA charts. The basic idea of the AEWMA scheme is to dynamically weight the past observations according to a suitable function of the current prediction error. This article extends the idea of the AEWMA chart for monitoring process locations to the case of monitoring process dispersion. A Markov chain model is established to analyze and design the suggested chart. It is shown that the AEWMA dispersion chart performs better than the EWMA and other dispersion charts in terms of its ability to perform relatively well at both small and large changes in process dispersion.

Keywords:

• Average run length
• Statistical process control
• Markov chain
• Normalizing transformation
• Dispersion charts

Acknowledgements

The authors would like to thank the editor and two referees for their valuable comments. L. Shu's work was supported in part by the Research Committee under the grant RG013/05-065.

Additional information

Notes on contributors

Lianjie Shu

Email: [email protected]