On increasing the sensitivity of mixed EWMA–CUSUM control charts for location parameter

Jimoh Olawale Ajadi · Muhammad Riaz · Khalid S. Al-Ghamdi

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Advanced Statistical Process Monitoring Probability and Statistical Research Fault Detection and Control Systems

Abstract

Control chart is an important statistical technique that is used to monitor the quality of a process. Shewhart control charts are used to detect larger disturbances in the process parameters, whereas cumulative sum (CUSUM) and exponential weighted moving average (EWMA) are meant for smaller and moderate changes. In this study, we enhanced mixed EWMA–CUSUM control charts with varying fast initial response (FIR) features and also with a runs rule of two out of three successive points that fall above the upper control limit. We investigate their run-length properties. The proposed control charting schemes are compared with the existing counterparts including classical CUSUM, classical EWMA, FIR CUSUM, FIR EWMA, mixed EWMA–CUSUM, 2/3 modified EWMA, and 2/3 CUSUM control charting schemes. A case study is presented for practical considerations using a real data set.

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References

1992Introduction to Statistical Quality Control 1954CONTINUOUS INSPECTION SCHEMES 1959Control Chart Tests Based on Geometric Moving Averages 2000Control Chart Tests Based on Geometric Moving Averages 2000Fast Initial Response for CUSUM Quality-Control Schemes: Give Your CUSUM A Head Start 1982Fast Initial Response for CUSUM Quality-Control Schemes: Give Your CUSUM A Head Start 1997Introduction to Statistical Quality Control 1999EWMA Control Charts with Time-Varying Control Limits and Fast Initial Response

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2018Enhancing the performance of the EWMA control chart for monitoring the process mean using auxiliary information 2017A HEWMA-CUSUM control chart for the Weibull distribution 2019Auxiliary Information Based Mixed EWMA–CUSUM Mean Control Chart with Measurement Error 2019Multivariate Mixed EWMA-CUSUM Control Chart for Monitoring the Process Variance-Covariance Matrix 2020An efficient approach of designing distribution-free exponentially weighted moving average schemes with dynamic fast initial response for joint monitoring of location and scale

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