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

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.

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

Symmetry ◽

10.3390/sym11010053 ◽

2019 ◽

Vol 11 (1) ◽

pp. 53 ◽

Cited By ~ 2

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.

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

Journal of Mathematics ◽

10.1155/2020/7680286 ◽

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

Mathematical Problems in Engineering ◽

10.1155/2020/5682587 ◽

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.

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