Designing a Repetitive Group Sampling Plan for Weibull Distributed Processes

Yan, Aijun; Liu, Sanyang

doi:10.1155/2016/5862071

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…The sampling plans for two parameter Weibull distribution have been discussed by Fertig et al [6] and another version of the Weibull sampling plans has been discussed by Jun et al [7]. The repetitive group acceptance sampling plan for Weibull distribution is given by Yan et al [8]. The sampling plan under progressive type I censoring for Weibull distribution has been discussed by Ding et al [9], among others.…”

Section: Introductionmentioning

confidence: 99%

Efficient Process Monitoring Under General Weibull Distribution

Shahbaz¹,

Shahbaz²

2022

Computer Systems Science and Engineering

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Product testing is a key ingredient in maintaining the quality of a production process. The production process is considered an efficient process if it is capable of quick identification of faulty products. The items produced by any production process are usually packed and acceptance or rejection of the pack depends upon its conformity to some specified quality level. Generally, the specified quality level is based upon the number of defective items found in the inspected number of items. Such decisions are based upon some rules and usually acceptance of the pack is based upon a fewer number of defective items in the pack. Such questions can be answered by using acceptance sampling plans. The acceptance sampling plans assume the fact that the quality level of the item follows some probability distribution. The sampling plans based upon some classical probability distributions are available but often it happens that the quality behavior of the product does not follow a simple probability model and hence the available sampling plans fail. In this paper, we have developed acceptance sampling plans when the product life follows a general Weibull distribution. The sampling plans have been constructed by considering the crisp and fuzzy behavior of the acceptance probability. These sampling plans have been constructed by assuming an infinite lot size. It has been found that the number of items required for inspection decreases with an increase in some parameters.

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Section: Introductionmentioning

confidence: 99%

Efficient Process Monitoring Under General Weibull Distribution

Shahbaz¹,

Shahbaz²

2022

Computer Systems Science and Engineering

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“…Reference [ 16 ] developed generalized inverted exponential distributions. References [ 17 – 19 ] studied the repetitive sampling plan under different situations.…”

Section: Introductionmentioning

confidence: 99%

Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy

Rao

Aslam

2021

BMC Med Res Methodol

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Background This research work is elaborated investigation of COVID-19 data for Weibull distribution under indeterminacy using time truncated repetitive sampling plan. The proposed design parameters like sample size, acceptance sample number and rejection sample number are obtained for known indeterminacy parameter. Methods The plan parameters and corresponding tables are developed for specified indeterminacy parametric values. The conclusion from the outcome of the proposed design is that when indeterminacy values increase the average sample number (ASN) reduces. Results The proposed repetitive sampling plan methodology application is given using COVID-19 data belong to Italy. The efficiency of the proposed sampling plan is compared with the existing sampling plans. Conclusions Using the tables and COVID-19 data illustration, it is concluded that the proposed plan required a smaller sample size as examined with the available sampling plans in the literature.

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“…More details about sampling plans using the process loss index can be seen in [3][4][5]. For other variable sampling plans, see [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A New Sampling Plan Using Neutrosophic Process Loss Consideration

Aslam

2018

Symmetry

114

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The theory of classical statistics assumes crisp, certain, and clear observations/parameters in engineering applications. However, in such engineering applications, due to their complex functions, it may not possible to obtain clear or crisp values of certain parameters. So, there is a chance of obtaining indeterminate, imprecise, vague, and incomplete parameters. In this situation, neutrosophic statistics can be applied, which is the generalization of classical statistics. This is reduced to classical statistics when no parameters are found to be indeterminate, imprecise, vague, or incomplete in actual practice. In this paper, we design a new sampling plan using the neutrosophic approach for the process loss function. The neutrosophic non-linear problem is given and applied to determine the neutrosophic plan parameters of the proposed sampling plan. A table is given and discussed with the help of factory data.

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Designing a Repetitive Group Sampling Plan for Weibull Distributed Processes

Cited by 13 publications

References 25 publications

Efficient Process Monitoring Under General Weibull Distribution

Efficient Process Monitoring Under General Weibull Distribution

Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy

A New Sampling Plan Using Neutrosophic Process Loss Consideration

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