Mixed memory control chart based on auxiliary information for simultaneously monitoring of process parameters: An application in glass field

2021 ◽  
pp. 107284
Author(s):  
Syed Masroor Anwar ◽  
Muhammad Aslam ◽  
Babar Zaman ◽  
Muhammad Riaz
Keyword(s):  
Control Chart ◽  
Process Parameters ◽  
Auxiliary Information ◽  
Memory Control

On mixed memory control charts based on auxiliary information for efficient process monitoring

2020 ◽  
Vol 36 (6) ◽  
pp. 1949-1968 ◽  
Author(s):  
Syed Masroor Anwar ◽  
Muhammad Aslam ◽  
Muhammad Riaz ◽  
Babar Zaman
Keyword(s):  
Process Monitoring ◽  
Control Charts ◽  
Auxiliary Information ◽  
Efficient Process ◽  
Memory Control

scholarly journals The Expected Average Run Length of the EWMA Median Chart with Estimated Process Parameters

2020 ◽  
Vol 49 (3) ◽  
pp. 19-24
Author(s):  
Huay Woon You ◽  
Michael Khoo Boon Chong ◽  
Chong Zhi Lin ◽  
Teoh Wei Lin
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Process Parameters ◽  
Average Run Length ◽  
Moving Average ◽  
Background Knowledge ◽  
Exponentially Weighted Moving Average ◽  
Control Phase ◽  
Run Length ◽  
Weighted Moving Average

The performance of a control chart is commonly investigated based on the assumption of known process parameters. Nevertheless, in most manufacturing and service applications, the process parameters are usually unknown to practitioners. Hence, they are estimated from an in-control Phase-I samples. As such, the performance of the control chart with estimated process parameters will behave differently from the corresponding chart with known process parameters. To study this issue, the exponentially weighted moving average (EWMA) median chart is examined in this article. The EWMA median chart is traditionally investigated based on the average run length (ARL). The limitation of the ARL is that it requires practitioners to specify the shift size in advance. This phenomenon is not ideal for practitioners who do not have background knowledge of the process. In view of this, the EWMA median chart with known and estimated process parameters is studied based on the ARL and expected average run length (EARL). The results indicate that as long as the particular shift size is within the range of shifts, the performance of the chart is almost the same, for the EWMA median chart with known and estimated process parameters.

A new Max-HEWMA control chart using auxiliary information

2018 ◽  
Vol 49 (5) ◽  
pp. 1285-1305 ◽  
Author(s):  
Amjad Javaid ◽  
Muhammad Noor-ul-Amin ◽  
Muhammad Hanif
Keyword(s):  
Control Chart ◽  
Auxiliary Information

scholarly journals On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 706 ◽  
Author(s):  
Shahid Hussain ◽  
Sun Mei ◽  
Muhammad Riaz ◽  
Saddam Akber Abbasi
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Charts ◽  
Auxiliary Information ◽  
Location Parameter ◽  
Attractive Alternative ◽  
Location Parameters ◽  
The Mean ◽  
Two Phases

A control chart is often used to monitor the industrial or services processes to improve the quality of the products. Mostly, the monitoring of location parameters, both in Phase I and Phase II, is done using a mean control chart with the assumption that the process is free from outliers or the estimators are correctly estimated from in-control samples. Generally, there are question marks about such kind of narratives. The performance of the mean chart is highly affected in the presence of outliers. Therefore, the median chart is an attractive alternative to the mean chart in this situation. The control charts are usually implemented in two phases: Phase I (retrospective) and Phase II (prospective/monitoring). The efficiency of any control chart in Phase II depends on the accuracy of control limits obtained from Phase I. The current study focuses on the Phase I analysis of location parameters using median control charts. We examined the performance of different auxiliary information-based median control charts and compared the results with the usual median chart. Standardized variance and relative efficacy are used as performance measures to evaluate the efficiency of median estimators. Moreover, the probability to signal measure is used to evaluate the performance of proposed control charts to detect any potential changes in the process. The results revealed that the proposed auxiliary information based median control charts perform better in Phase I analysis. In addition, a practical illustration of an industrial scenario demonstrated the significance of the proposed control charts, in which the monitoring of concrete compressive strength is emphasized.

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