Double Moving Average Control Chart for Autocorrelated Data
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The assumption of normality and independence is necessary for statistical inference of control charts. Misleading results could be obtained if the traditional control chart technique is applied to the autocorrelated data. A time series model is employed to produce optimum output when data is correlated. The objective of this current research is to create a new control chart methodology which takes the autocorrelation data observations into account. Charts of moving average, exponentially weighted, and cumulative sum perform better for the autocorrelation of data for small and moderate changes. The proposed methodology is highly skilled and receptive to minor, moderate, and major changes in the process. The proposed DMA chart increases the efficiency of the average run length (ARL) chart for moving average (MA) to detect the small to medium magnitude shifts in the mean. The simulation also demonstrated that the DMA chart with spans of w=10 and 15 generally performs better in terms of average run length (ARL) as compared to classical MA. This research might be extended to a multivariate autocorrelated statistical process control but it could also be used to recognize and categorize seven categories of traditional control chart patterns, such as downward, upward shift, normal trend, cyclic, systematic patterns, increasing and decreasing trend. In order to identify and categorise a set of subclasses of abnormal patterns, this model (multivariate autocorrelated statistical process control chart) should employ a multilayer feed-forward Artificial Neural Network (ANN) architecture controlled by a back-propagation learning rule.
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