Optimal Design of Reliability Acceptance Sampling Plan Based on Sequential Order Statistics

Kumar, M.; Ramyamol, P. C.

doi:10.1515/eqc-2019-0012

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Therefore, the accelerated stress level is firstly taken as a decision variable, and based on the consistency of the failure mechanism, it is selected within a certain range. Then, under the producer's risk and consumer's risk constraints, a new optimization model can be established by minimizing

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to obtain the optimum AASP, which includes the sampling plan and the decision‐making criterion 26 …”

Section: Introductionmentioning

confidence: 99%

“…Then, under the producer's risk and consumer's risk constraints, a new optimization model can be established by minimizing 𝑇𝐶 to obtain the optimum AASP, which includes the sampling plan and the decision-making criterion. 26 In summary, existing works ignored the impact of different possibilities of the AF's value and designed the optimum AASPs under the fixed accelerated stress level, which are not consistent with engineering practices. Consequently, to solve the above problems, considering the uncertainty of AF, this study proposes a new optimum AASP design method based on CSALT.…”

Section: Introductionmentioning

confidence: 99%

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Optimal design of reliability acceptance sampling plan based on constant‐stress accelerated life testing considering the uncertainty of accelerated factor

Lou

Zheng

Yang

2023

Quality & Reliability Eng

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With the increasing marketplace demand for decreasing test time and cost, constant-stress accelerated life testing (CSALT) is widely used for reliability demonstration in practice. The existing accelerated acceptance sampling plans (AASPs) do not consider the uncertainty of accelerated factor (AF), which makes it difficult to accurately describe the producer's and consumer's risks. Previous AASP studies on CSALT were mainly carried out under a fixed accelerated stress level, and the influence of accelerated stress level is not concerned. Therefore, considering the uncertainty of AF and taking the lognormal distribution as an example, the AASP design method with time-censoring is proposed, where the life-stress relationship between the location parameter and stress level is expressed by an inverse power law model. First, considering the uncertainty of AF expressed by a distribution, the acceptance probability can be obtained by the mathematic expectation of conditional acceptance probability, and the producer's and consumer's risks can be accurately expressed. Then, to reduce the total cost furtherly, the accelerated stress level is considered as a new decision variable, the optimum AASP including the sampling plan and the decisionmaking criterion can be designed by minimizing the total cost and solving the risk constraint equations of both sides. A real case study of white organic light-emitting diodes is carried out to verify the effectiveness of the proposed method.

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to obtain the optimum AASP, which includes the sampling plan and the decision‐making criterion 26 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal design of reliability acceptance sampling plan based on constant‐stress accelerated life testing considering the uncertainty of accelerated factor

Lou

Zheng

Yang

2023

Quality & Reliability Eng

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Some inferences on a mixture of exponential and Rayleigh distributions based on fuzzy data

Mathai

Kumar

2023

IJQRM

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PurposeIn this paper, a mixture of exponential and Rayleigh distributions in the proportions α and 1 − α and all the parameters in the mixture distribution are estimated based on fuzzy data.Design/methodology/approachThe methods such as maximum likelihood estimation (MLE) and method of moments (MOM) are applied for estimation. Fuzzy data of triangular fuzzy numbers and Gaussian fuzzy numbers for different sample sizes are considered to illustrate the resulting estimation and to compare these methods. In addition to this, the obtained results are compared with existing results for crisp data in the literature.FindingsThe application of fuzziness in the data will be very useful to obtain precise results in the presence of vagueness in data. Mean square errors (MSEs) of the resulting estimators are computed using crisp data and fuzzy data. On comparison, in terms of MSEs, it is observed that maximum likelihood estimators perform better than moment estimators.Originality/valueClassical methods of obtaining estimators of unknown parameters fail to give realistic estimators since these methods assume the data collected to be crisp or exact. Normally, such case of precise data is not always feasible and realistic in practice. Most of them will be incomplete and sometimes expressed in linguistic variables. Such data can be handled by generalizing the classical inference methods using fuzzy set theory.

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Optimal Design of Reliability Acceptance Sampling Plan Based on Sequential Order Statistics

Cited by 2 publications

References 11 publications

Optimal design of reliability acceptance sampling plan based on constant‐stress accelerated life testing considering the uncertainty of accelerated factor

Optimal design of reliability acceptance sampling plan based on constant‐stress accelerated life testing considering the uncertainty of accelerated factor

Some inferences on a mixture of exponential and Rayleigh distributions based on fuzzy data

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