A new double EWMA‐t chart with auxiliary information for the process mean

2021 ◽  
Author(s):  
Abdul Haq ◽  
Sana Ejaz ◽  
Ming Ha Lee ◽  
Manzoor Khan
Keyword(s):  
Auxiliary Information ◽  
Process Mean

scholarly journals Exponentially Weighted Moving Average Control Charts for the Process Mean Using Exponential Ratio Type Estimator

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hina Khan ◽  
Saleh Farooq ◽  
Muhammad Aslam ◽  
Masood Amjad Khan
Keyword(s):  
Control Charts ◽  
Sampling Design ◽  
Control Structure ◽  
Moving Average ◽  
Auxiliary Information ◽  
Ranked Set Sampling ◽  
Process Mean ◽  
Average Control ◽  
The Mean ◽  
Weighted Moving Average

This study proposes EWMA-type control charts by considering some auxiliary information. The ratio estimation technique for the mean with ranked set sampling design is used in designing the control structure of the proposed charts. We have developed EWMA control charts using two exponential ratio-type estimators based on ranked set sampling for the process mean to obtain specific ARLs, being suitable when small process shifts are of interest.

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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