An attribute control chart for multivariate Poisson distribution using multiple dependent state repetitive sampling

2018 ◽  
Author(s):  
Mansour Sattam Aldosari ◽  
Muhammad Aslam ◽  
Gadde Srinivasa Rao ◽  
Chi‐Hyuck Jun
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Multivariate Poisson Distribution ◽  
Dependent State

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

A control chart for multivariate Poisson distribution using repetitive sampling

2016 ◽  
Vol 44 (1) ◽  
pp. 123-136 ◽  
Author(s):  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Liaquat Ahmad ◽  
Chi-Hyuck Jun
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Multivariate Poisson Distribution

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.

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