scholarly journals Shewhart Attribute and Variable Control Charts Using Modified Multiple Dependent State Sampling

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 53 ◽  
Author(s):  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Mohammed Albassam
Keyword(s):  
Normal Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Practical Application ◽  
Attribute Data ◽  
Variable Control ◽  
Run Lengths ◽  
Different Levels ◽  
Dependent State ◽  
Selection Of

In this article, modified multiple dependent (or deferred) state sampling control charts for the attribute and the variable quality characteristics are presented. The proposed control charts are designed using the symmetry property of the normal distribution. The control chart coefficients are estimated through simulation at different levels of the parameters using the normal distribution. The proposed control chart scheme is evaluated by calculating the in-control average run lengths and out-of-control average run lengths. Tables are constructed for the selection of parameters for different control limit coefficients under several shift levels for the attribute data as well as the variable data. Examples are included for the practical application of the proposed control chart schemes. The proposed control chart scheme is also compared with the existing control charts. It has been observed that the proposed schemes are better in quick detection of the out-of-control processes.

scholarly journals Design of a New Variable Shewhart Control Chart Using Multiple Dependent State Repetitive Sampling

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 641 ◽  
Author(s):  
Mansour Sattam Aldosari ◽  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Chi-Hyuck Jun
Keyword(s):  
Normal Distribution ◽  
Control Chart ◽  
Symmetry Property ◽  
Shewhart Control Chart ◽  
Variable Control ◽  
Shewhart Control ◽  
Run Lengths ◽  
Dependent State ◽  
Better Than

In this paper, a new variable control chart is proposed using multiple dependent-state repetitive sampling by assuming that the data follows a normal distribution having a symmetry property. Its efficiency will be evaluated in terms of in-control and out-of-control average run lengths. The results showed that the proposed chart is better than the existing variable control chart to detect an early shift in the process. An industrial example is given to illustrate the proposed chart in the industry.

scholarly journals Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Muhammad Saleem ◽  
Rehan Ahmad Khan Sherwani ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Real Life ◽  
Quality Characteristics ◽  
Statistical Quality Control ◽  
Nonnormal Distributions ◽  
Run Lengths ◽  
Chart Design ◽  
Dependent State

More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

scholarly journals Process Monitoring for Gamma Distributed Product under Neutrosophic Statistics Using Resampling Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Abdullah M. Almarashi ◽  
Muhammad Aslam
Keyword(s):  
Control Chart ◽  
Simulation Study ◽  
Average Run Length ◽  
Control Process ◽  
Practical Application ◽  
Interval Method ◽  
Length Measurements ◽  
Neutrosophic Statistics ◽  
Run Lengths ◽  
Different Levels

In this article, a repetitive sampling control chart for the gamma distribution under the indeterminate environment has been presented. The control chart coefficients, probability of in-control, probability of out-of-control, and average run lengths have been determined under the assumption of the symmetrical property of the normal distribution using the neutrosophic interval method. The performance of the designed chart has been evaluated using the average run length measurements under different process settings for an indeterminate environment. In-control and out-of-control nature of the proposed chart under different levels of shifts have been described. The comparison of the proposed chart has been made with the existing chart. A real-world example from the healthcare department has been included for the practical application of the proposed chart. It has been observed from the simulation study and real example that the proposed control chart is efficient in quick monitoring of the out-of-control process. It can be concluded that the proposed control chart can be applied effectively in uncertainty.

scholarly journals A Control Chart for Exponentially Distributed Characteristics Using Modified Multiple Dependent State Sampling

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Ahmad O. Albazli ◽  
Muhammad Aslam ◽  
Saeed A. Dobbah
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Real Data ◽  
Detection Ability ◽  
Previous Sample ◽  
Current Sample ◽  
Run Lengths ◽  
Control Limits ◽  
Dependent State ◽  
Better Than

In this paper, a t-control chart based on modified multiple dependent state sampling is proposed for monitoring processes that assume time between events following exponential distribution. The chart has double control limits and employs information from a previous sample and the current sample. The control chart coefficient “constants” are estimated by considering different values of the in-control average run lengths. The detection ability of the proposed control chart is found to be better than that of control charts based on multiple dependent state sampling in terms of average run lengths and the standard deviation of run lengths and better than generalized multiple dependent state sampling in terms of average run lengths. Case studies with real data are included as illustrative examples for the implementation of the proposed chart.

scholarly journals Monitoring process mean with a new EWMA control chart

Production ◽  
2011 ◽  
Vol 21 (2) ◽  
pp. 217-222 ◽  
Author(s):  
Yang Su-Fen ◽  
Tsai Wen-Chi ◽  
Huang Tzee-Ming ◽  
Yang Chi-Chin ◽  
Cheng Smiley
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Population Distribution ◽  
Average Run Length ◽  
Process Data ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Simple Statistic ◽  
Run Lengths ◽  
Proper Values

In practice, sometimes the process data did not come from a known population distribution. So the commonly used Shewhart variables control charts are not suitable since their performance could not be properly evaluated. In this paper, we propose a new EWMA Control Chart based on a simple statistic to monitor the small mean shifts in the process with non-normal or unknown distributions. The sampling properties of the new monitoring statistic are explored and the average run lengths of the proposed chart are examined. Furthermore, an Arcsine EWMA Chart is proposed since the average run lengths of the Arcsine EWMA Chart are more reasonable than those of the new EWMA Chart. The Arcsine EWMA Chart is recommended if we are concerned with the proper values of the average run length.

scholarly journals A Variable Control Chart Based on Process Capability Index Under Generalized Multiple Dependent State Sampling

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 34031-34044 ◽  
Author(s):  
G. Srinivasa Rao ◽  
Muhammad Ali Raza ◽  
Muhammad Aslam ◽  
Ali Hussein AL-Marshadi ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Process Capability ◽  
Process Capability Index ◽  
Capability Index ◽  
Variable Control ◽  
Dependent State

scholarly journals Multiple Dependent State Sampling-Based Chart Using Belief Statistic under Neutrosophic Statistics

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ahmed Ibrahim Shawky ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Control Process ◽  
Real Data ◽  
Process Conditions ◽  
Neutrosophic Statistics ◽  
The Mean ◽  
Run Lengths ◽  
Dependent State ◽  
Better Than

In this paper, a control chart scheme has been introduced for the mean monitoring using gamma distribution for belief statistics using multiple dependent (deferred) state sampling under the neutrosophic statistics. The coefficients of the control chart and the neutrosophic average run lengths have been estimated for specific false alarm probabilities under various process conditions. The offered chart has been compared with the existing classical chart through simulation and the real data. From the comparison, it is concluded that the performance of the proposed chart is better than that of the existing chart in terms of average run length under uncertain environment. The proposed chart has the ability to detect a shift quickly than the existing chart. It has been observed that the proposed chart is efficient in quick monitoring of the out-of-control process and a cherished addition in the toolkit of the quality control personnel.

Design of EWMA Control Charts for Controlling Dependent Process Stages with Attribute Data

2013 ◽  
Vol 411-414 ◽  
pp. 1085-1088
Author(s):  
Chung Ming Yang ◽  
Su Fen Yang ◽  
Jin Tyan Yeh
Keyword(s):  
Control Chart ◽  
Simulation Study ◽  
Control Charts ◽  
Detection Ability ◽  
Numerical Example ◽  
Attribute Data ◽  
Dependent Process

In this study, we propose EWMA control charts to monitor two dependent process stages with attribute data. The detection ability of the EWMA control charts is compared to those of Shewhart attribute control charts and cause selecting control chart by different correlation. Numerical example and simulation study show that the EWMA control charts have better performance compared to Shewhart attribute control charts and cause selecting control charts.

scholarly journals SAMPLE STANDARD DEVIATION(s) CHART UNDER THE ASSUMPTION OF MODERATENESS AND ITS PERFORMANCE ANALYSIS

2017 ◽  
Vol 5 (6) ◽  
pp. 368-377
Author(s):  
Kalpesh S. Tailor
Keyword(s):  
Standard Deviation ◽  
Performance Analysis ◽  
Normal Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Curve Analysis ◽  
Mean Deviation ◽  
Sample Standard Deviation ◽  
Underlying Distribution ◽  
Control Limits

Moderate distribution proposed by Naik V.D and Desai J.M., is a sound alternative of normal distribution, which has mean and mean deviation as pivotal parameters and which has properties similar to normal distribution. Mean deviation (δ) is a very good alternative of standard deviation (σ) as mean deviation is considered to be the most intuitively and rationally defined measure of dispersion. This fact can be very useful in the field of quality control to construct the control limits of the control charts. On the basis of this fact Naik V.D. and Tailor K.S. have proposed 3δ control limits. In 3δ control limits, the upper and lower control limits are set at 3δ distance from the central line where δ is the mean deviation of sampling distribution of the statistic being used for constructing the control chart. In this paper assuming that the underlying distribution of the variable of interest follows moderate distribution proposed by Naik V.D and Desai J.M, 3δ control limits of sample standard deviation(s) chart are derived. Also the performance analysis of the control chart is carried out with the help of OC curve analysis and ARL curve analysis.

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