A homogenously weighted moving average scheme for observations under the effect of serial dependence and measurement inaccuracy
Loading...
Date
Authors
Thanwane, Maonatlala
Shongwe, Sandile C.
Aslam, Muhammad
Malela-Majika, Jean-Claude
Albassam, Mohammed
Journal Title
Journal ISSN
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.
Description
Keywords
Autocorrelation, Control chart, Homogeneously weighted moving, Homogeneously weighted moving average (HWMA), Measurement errors, Mixed samples strategy, Multiple measurements, Skipping sampling strategy
Sustainable Development Goals
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.