scholarly journals CUSUM control charts with variable sampling interval for monitoring the ratio of two normal variables

2019 ◽  
Vol 36 (2) ◽  
pp. 474-497 ◽  
Author(s):  
Huu Du Nguyen ◽  
Kim Phuc Tran ◽  
Henri L. Heuchenne
Keyword(s):  
Control Charts ◽  
Sampling Interval ◽  
Variable Sampling Interval ◽  
Variable Sampling ◽  
Cusum Control Charts

Design of a Variable Sampling Interval EWMA Median Control Chart

2019 ◽  
Vol 26 (05) ◽  
pp. 1950021 ◽  
Author(s):  
Kim Phuc Tran ◽  
Philippe Castagliola ◽  
Thi Hien Nguyen ◽  
Anne Cuzol
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Food Production ◽  
Average Run Length ◽  
Moving Average ◽  
Sampling Interval ◽  
Performance Comparison ◽  
Variable Sampling Interval ◽  
Variable Sampling ◽  
Weighted Moving Average

In the literature, median type control charts have been widely investigated as easy and efficient means to monitor the process mean when observations are from a normal distribution. In this work, a Variable Sampling Interval (VSI) Exponentially Weighted Moving Average (EWMA) median control chart is proposed and studied. The Markov chains are used to calculate the average run length to signal (ARL). A performance comparison with the original EWMA median control chart is made. The numerical results show that the proposed chart is considerably more effective as it is faster in detecting process shifts. Finally, the implementation of the proposed chart is illustrated with an example in food production process.

A combined variable sampling interval and double sampling control chart with auxiliary information for the process mean

2019 ◽  
Vol 42 (6) ◽  
pp. 1151-1165 ◽  
Author(s):  
Adamu A Umar ◽  
Michael BC Khoo ◽  
Sajal Saha ◽  
Abdul Haq
Keyword(s):  
Steady State ◽  
Control Charts ◽  
Auxiliary Information ◽  
Sampling Interval ◽  
Double Sampling ◽  
Process Mean ◽  
Small Shift ◽  
Average Time To Signal ◽  
Variable Sampling Interval ◽  
Variable Sampling

In recent years, the suitable use of auxiliary information technique in control charts has shown an improved run length performance compared to control charts that do not have this feature. This article proposes a combined variable sampling interval (VSI) and double sampling (DS) chart using the auxiliary information (AI) technique (called VSIDS-AI chart, hereafter). The plotting-statistic of the VSIDS-AI chart requires information from both the study and auxiliary variables to efficiently detect process mean shifts. The charting statistics, optimal design and performance assessment of the VSIDS-AI chart are discussed. The steady-state average time to signal (ssATS) and steady-state expected average time to signal (ssEATS) are considered as the performance measures. The ssATS and ssEATS results of the VSIDS-AI chart are compared with those of the DS AI, variable sample size and sampling interval AI, exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The results of comparison show that the VSIDS-AI chart outperforms the charts under comparison for all shift sizes, except the EWMA-AI and RS-AI charts for small shift sizes. An illustrative example is provided to demonstrate the implementation of the VSIDS-AI chart.

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