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.

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 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.

Hotelling T2 chart using the generalized multiple dependent state sampling scheme

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandra García-Bustos ◽  
Joseph León ◽  
María Nela Pastuizaca
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Control Process ◽  
Sampling Scheme ◽  
Real Problem ◽  
Optimal Parameters ◽  
Content Type ◽  
Multivariate Control Chart ◽  
Multivariate Control ◽  
Dependent State

PurposeThis research proposes a multivariate control chart, whose parameters are optimized using genetic algorithms (GA) in order to accelerate the detection of a change in the vector of means.Design/methodology/approachThis chart is based on a variation of the Hotelling T2 chart using a sampling scheme called generalized multiple dependent state sampling. For the analysis of performances of this chart, the out-of-control average run length (ARL) values were used for different scenarios. In this comparison, it was considered the classic Hotelling T2 chart and the T2 chart using the scheme called multiple dependent state sampling.FindingsIt was observed that the new chart with its optimized parameters is more efficient to detect an out-of-control process. Additionally, a sensitivity analysis was performed, and it was concluded that the best yields are obtained when the change to be considered in the optimization is small. An application in the resolution of a real problem is given.Originality/valueIn this research, a multivariate control chart is proposed based on the Hotelling T2 statistic but adding a sampling scheme. This makes this control chart more efficient than the classic T2 chart because the new chart not only uses the current information of the T2 statistic but also conditions the decision to consider a process as “in- control” on the statistic's previous information. The practitioner can obtain the optimal parameters of this new chart through a friendly program developed by the authors.

scholarly journals A new sudden death chart for the Weibull distribution under complexity

2021 ◽  
Author(s):  
Osama H. Arif ◽  
Muhammad Aslam
Keyword(s):  
Sudden Death ◽  
Control Chart ◽  
Control Process ◽  
Value Addition ◽  
Detection Ability ◽  
Classical Statistics ◽  
Monitoring Reliability ◽  
Neutrosophic Statistics ◽  
Industrial Use ◽  
Run Lengths

AbstractThis article presents a new control chart for monitoring reliability using sudden death testing under the neutrosophic statistics (NS). The average run lengths of the in-control and the out-of-control process have been determined for evaluating the quick detection ability for small and moderate shifts. For the industrial use, tables and figures have been presented for different parameters. The proposed control chart is efficient in comparison with the existing control chart under classical statistics and value addition in the toolkit of the quality control personnel.

scholarly journals Monitoring the Variability in the Process Using Neutrosophic Statistical Interval Method

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 562 ◽  
Author(s):  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Muhammad Khan
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Interval Methods ◽  
Interval Method ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
Imprecise Observations ◽  
Better Than

Existing variance control charts are designed under the assumptions that no uncertain, fuzzy and imprecise observations or parameters are in the population or the sample. Neutrosophic statistics, which is the extension of classical statistics, has been widely used when there is uncertainty in the data. In this paper, we will originally design S 2 control chart under the neutrosophic interval methods. The complete structure of the neutrosophic S 2 control chart will be given. The necessary measures of neutrosophic S 2 will be given. The neutrosophic coefficient of S 2 control chart will be determined through the neutrosophic algorithm. Some tables are given for practical use. The efficiency of the proposed control chart is shown over the S 2 control chart designed under the classical statistics in neutrosophic average run length (NARL). A real example is also added to illustrate the proposed control chart. From the comparison in the simulation study and case study, it is concluded that the proposed control chart performs better than the existing control chart under uncertainty.

scholarly journals A New Control Chart for Monitoring the Process Mean Using Successive Sampling and Multiple Dependent State Repetitive Sampling

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 70
Author(s):  
Mansour Sattam Aldosari ◽  
Muhammad Aslam ◽  
Chi-Hyuck Jun ◽  
Khushnoor Khan
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Artificial Data ◽  
Process Mean ◽  
Data Set ◽  
Successive Sampling ◽  
Shift Detection ◽  
Dependent State ◽  
Better Than

In this paper, a new control chart scheme has been developed for monitoring the production process mean using successive sampling over two occasions. The proposed chart reduces to three different existing control charts under different assumptions and is compared with these three existing control charts for monitoring the process average. It has been observed that the proposed control chart performs better than the other existing control charts in terms of average run length (ARL). A simulation study using an artificial data set was included for demonstrating the process shift detection power of the proposed control chart.

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.

An Attribute np Control Chart for Monitoring Mean Life Using Multiple Deferred State Sampling Based on Truncated Life Tests

2019 ◽  
Vol 26 (01) ◽  
pp. 1950004
Author(s):  
S. Balamurali ◽  
P. Jeyadurga
Keyword(s):  
Control Chart ◽  
Pareto Distribution ◽  
Average Run Length ◽  
Run Length ◽  
Manufacturing Process Control ◽  
The Status ◽  
Truncated Life Test ◽  
Life Tests ◽  
The Mean ◽  
Run Lengths

In this paper, we design an attribute [Formula: see text] control chart for monitoring the mean life of the product where the lifetime follows the Pareto distribution of the second kind. The lifetime of the product is determined by time truncated life test and the multiple deferred state sampling is used to declare the status of the manufacturing process. Control limit coefficients and multiple deferred state sampling parameters such as sample size and number of successive subgroups required to declare the state of the process are determined such that the in-control average run length is as near as possible to the target average run length. Out-of-control average run lengths are calculated for the determined parameters using various shift constants. The performance of the chart is compared with other existing chart in terms of average run length.

scholarly journals Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 154
Author(s):  
Anderson Fonseca ◽  
Paulo Henrique Ferreira ◽  
Diego Carvalho do Nascimento ◽  
Rosemeire Fiaccone ◽  
Christopher Ulloa-Correa ◽  
...  
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Atacama Desert ◽  
Real Data ◽  
Interval Data ◽  
Total Quality ◽  
Statistical Process ◽  
Monte Carlo Simulation Study ◽  
Run Length ◽  
Study Results

Statistical monitoring tools are well established in the literature, creating organizational cultures such as Six Sigma or Total Quality Management. Nevertheless, most of this literature is based on the normality assumption, e.g., based on the law of large numbers, and brings limitations towards truncated processes as open questions in this field. This work was motivated by the register of elements related to the water particles monitoring (relative humidity), an important source of moisture for the Copiapó watershed, and the Atacama region of Chile (the Atacama Desert), and presenting high asymmetry for rates and proportions data. This paper proposes a new control chart for interval data about rates and proportions (symbolic interval data) when they are not results of a Bernoulli process. The unit-Lindley distribution has many interesting properties, such as having only one parameter, from which we develop the unit-Lindley chart for both classical and symbolic data. The performance of the proposed control chart is analyzed using the average run length (ARL), median run length (MRL), and standard deviation of the run length (SDRL) metrics calculated through an extensive Monte Carlo simulation study. Results from the real data applications reveal the tool’s potential to be adopted to estimate the control limits in a Statistical Process Control (SPC) framework.

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