A homogenously weighted moving average scheme for observations under the effect of serial dependence and measurement inaccuracy

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Authors

Thanwane, Maonatlala
Shongwe, Sandile C.
Aslam, Muhammad
Malela-Majika, Jean-Claude
Albassam, Mohammed

Journal Title

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Volume Title

Publisher

Growing Science

Abstract

The combined effect of serial dependency and measurement errors is known to negatively affect the statistical efficiency of any monitoring scheme. However, for the recently proposed homogenously weighted moving average (HWMA) scheme, the research that exists concerns independent and identically distributed observations and measurement errors only. Thus, in this paper, the HWMA scheme for monitoring the process mean under the effect of within-sample serial dependence with measurement errors is proposed for both constant and linearly increasing measurement system variance. Monte Carlo simulation is used to evaluate the run-length distribution of the proposed HWMA scheme. A mixed- sampling strategy is incorporated to the HWMA scheme to reduce the negative effect of serial dependence and measurement errors and its performance is compared to the existing Shewhart scheme. An example is given to illustrate how to implement the proposed HWMA scheme for use in real-life applications.

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Keywords

Autocorrelation, Control chart, Homogeneously weighted moving, Homogeneously weighted moving average (HWMA), Measurement errors, Mixed samples strategy, Multiple measurements, Skipping sampling strategy

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Citation

Thanwane, M., Shongwe, S.C., Aslam, M. et al. 2021, 'A homogenously weighted moving average scheme for observations under the effect of serial dependence and measurement inaccuracy', International Journal of Industrial Engineering Computations, vol. 12, pp. 401-414.