A Control Chart for COM-Poisson Distribution Using Multiple Dependent State Sampling

2016 ◽  
Vol 32 (8) ◽  
pp. 2803-2812 ◽  
Author(s):  
Muhammad Aslam ◽  
Liaquat Ahmad ◽  
Chi-Hyuck Jun ◽  
Osama H. Arif
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Dependent State

On the Estimation Error in Zero-Inflated Poisson Model for Process Control

2003 ◽  
Vol 10 (02) ◽  
pp. 159-169 ◽  
Author(s):  
B. He ◽  
M. Xie ◽  
T. N. Goh ◽  
P. Ranjan
Keyword(s):  
Sample Size ◽  
Poisson Distribution ◽  
Control Chart ◽  
Average Run Length ◽  
Estimation Error ◽  
Accurate Estimate ◽  
Minimum Size ◽  
High Quality ◽  
Run Length ◽  
Run Length Distribution

The control chart based on a Poisson distribution has often been used to monitor the number of defects in sampling units. However, many false alarms could be observed due to extra zero counts, especially for high-quality processes. Therefore, some alternatives have been developed to alleviate this problem, one of which is the control chart based on the zero-inflated Poisson distribution. This distribution takes into account the extra zeros present in the data, and yield more accurate results than the Poisson distribution. However, implementing a control chart is often based on the assumption that the parameters are either known or an accurate estimate is available. For a high quality process, an accurate estimate may require a very large sample size, which is seldom available. In this paper the effect of estimation error is investigated. An analytical approximation is derived to compute shift detection probability and run length distribution. The study shows that the false alarm rates are higher than the desirable level for smaller values of the sample size. This is further supported by smaller average run length. In general, the quantitative results from this paper can be utilized to select a minimum size of the initial sample for estimating the control limits so that certain average run length requirements are met.

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.

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 A Control Chart Based on Moving Average Model Functioned for Poisson Distribution

2020 ◽  
Vol 3 (10) ◽  
Author(s):  
Hira Arooj ◽  
◽  
Khawar Iqbal Malik ◽  
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Current Control ◽  
Average Model ◽  
Time Intervals ◽  
Manufacture Process ◽  
Moving Average Model

A control chart used with MA control chart to track the number of faulty products or faults suggested. When the characteristics of quality of interest obey a Poisson distribution. A specified number of objects are observed at various time intervals in order to observe the number of non-conformities. By measuring ARLs under different sample sizes and parameters by considering ARLs in power, the output of the proposed chart is evaluated. It should be noted The proposed control chart seems to be morereliable than the traditional current control charts in detecting small adjustments in the manufacture process.

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.

Designing a control chart of extended EWMA statistic based on multiple dependent state sampling

2019 ◽  
Vol 47 (8) ◽  
pp. 1482-1492
Author(s):  
Muhammad Naveed ◽  
Muhammad Azam ◽  
Nasrullah Khan ◽  
Muhammad Aslam
Keyword(s):  
Control Chart ◽  
Dependent State
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