Efficient Homogeneously Weighted Moving Average Chart for Monitoring Process Mean Using an Auxiliary Variable
Nurudeen A. Adegoke,Adam N. H. Smith,Marti J. Anderson,Ridwan A. Sanusi,Matthew D. M. Pawley +4 more
TLDR
The proposed chart is a homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable.Abstract:
In this paper, we propose an efficient control chart for monitoring small shifts in a process mean for scenarios where the process variable is observed with a correlated auxiliary variable. The proposed chart, called an auxiliary homogeneously weighted moving average (AHWMA) chart, is a homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable. We provide the design structure of the chart and examine its performance in terms of its run length properties. Using a simulation study, we compare its run length performance with several existing methods for detecting a small shift in the process mean. Our simulation results show that the proposed chart is more efficient in detecting a small shift in the process mean than its competitors. We provide a detailed study of the chart's robustness to non-normal distributions and show that the chart may also be designed to be less sensitive to non-normality. We give some recommendations on the application of the chart when the process parameters are unknown and provide an example to show the implementation of the proposed new technique.read more
Citations
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GLM‐based control charts for the inverse Gaussian distributed response variable
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A mixed HWMA‐CUSUM mean chart with an application to manufacturing process
Muhammad Abid,Muhammad Abid,Sun Mei,Hafiz Zafar Nazir,Muhammad Riaz,Shahid Hussain,Shahid Hussain +6 more
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On mixed memory control charts based on auxiliary information for efficient process monitoring
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A New HWMA Dispersion Control Chart with an Application to Wind Farm Data
TL;DR: In this paper, the authors proposed a new one-sided HWMA chart to effectively detect small changes in the process dispersion in wind farm data, where run-length profiles like the average RL, the standard deviation RL, and the median RL were used as the performance measures.
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Statistical quality control : a modern introduction
TL;DR: Part I: Introduction Chapter 1: Quality Improvement in the Modern Business Environment Chapter 2: The DMAIC Process Chapter 3: Statistical Methods Useful in Quality Control and Improvement Chapter 4: Inferences about Process Quality