Planning reliability demonstration tests with limited expected risks

Fernández, Arturo J.

doi:10.1016/j.cie.2021.107918

Cited by 6 publications

(2 citation statements)

References 63 publications

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“…The Weibull distribution is a commonly applied lifetime distribution in RASP since it is a versatile distribution that can take on the characteristics of other types of distributions 30 . For instance, if

\kappa

= 1, the Weibull distribution reduces to an exponential distribution.…”

Section: Preliminariesmentioning

confidence: 99%

A modified reliability acceptance sampling plan with warranty return rate consideration under Weibull‐distributed accelerated lifetime data

Wang,

Shu

2024

Quality & Reliability Eng

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Warranty is a standard and essential policy for today's products. The period and cost of warranty are majorly affected by the reliability of products. Therefore, validation and verification of product reliability are required to control warranty costs and sustain the brand reputation. In this paper, we propose a modified reliability acceptance sampling plan (MRASP) for product reliability validation. The operating characteristic function of the MRASP is derived by considering the accelerated time‐to‐failure data with Type‐II censoring, which follows a Weibull distribution. To minimize the number of failures required for the accelerated life test and consider the sampling risk constraints, we constructed an optimization model to determine the optimal plan design of the proposed MRASP. Following the optimal plan design, practitioners can set the number of failures required in the accelerated life test to estimate the lifetime capability of the submitted lot and decide the lot disposition using the acceptance benchmark. In addition, the lifetime capability estimation can be used to infer the warranty return rate and warranty cost of the lot, which is valuable for practitioners to formulate the warranty budget. Finally, we illustrate a real‐world case to demonstrate the practicality and applicability of the proposed method.

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\kappa

= 1, the Weibull distribution reduces to an exponential distribution.…”

Section: Preliminariesmentioning

confidence: 99%

A modified reliability acceptance sampling plan with warranty return rate consideration under Weibull‐distributed accelerated lifetime data

Wang,

Shu

2024

Quality & Reliability Eng

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“…Typical lifetime models in reliability testing are the Weibull, gamma and lognormal distributions. Reliability test plans for Weibull models have been proposed by Jun et al [26], Chen et al [27], Arizono et al [28], Tsai et al [29], Seo et al [30], Aslam et al [31], Fernández [32], [33], Roy [34] and Gao et al [35]. If the Weibull shape parameter is 1, the distribution reduces to the exponential model, which plays an important role in reliability engineering; see, e.g., Aslam [36], Dey and Chakraborty [37], Fernández [38], [39], [40] and Lee et al [41], [42].…”

Section: Introductionmentioning

confidence: 99%

Overdispersion Effects on Reliability Test Planning

et al. 2023

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The impact of overdispersion on the design of optimal reliability demonstration test plans for beta-binomial models with Weibull, gamma and lognormal lifetime distributions is analyzed. Assuming limited producer and consumer risks, fixed-duration test plans with minimal sample sizes and optimalduration test plans with minimum costs based on failure count data are determined by solving the corresponding nonlinear programming problems when the fluctuation of the failure probability is described by a beta distribution. If the test time is fixed, the overdispersion effect on the optimal sample size and decision criterion is important in most situations. The influence is less relevant when the engineer also wants to find the minimum-cost test time. Optimal-duration plans usually outperform fixed-duration schemes in terms of costs and robustness against overdispersion. The use of lot inspection schemes with optimal reliability test times is strongly recommended when the presence of overdispersed failure count data is suspected. Applications of the developed methodology to the manufacturing of microelectronic chips and semiconductor lasers are provided for illustrative and comparative purposes.

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Comparing Risks for Binomial Reliability Assurance Test Planning

Kim,

Wilson

2024

Appl Stoch Models Bus & Ind

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Balancing consumer's and producer's risk is an important consideration when planning tests. Instead of focusing on finding a single best test plan, we introduce a general framework to systematically identify a set of binomial test plans by leveraging the inverse relationship between the two risks. The framework is applied to compare a variety of assurance testing frameworks, including classical tests, and Bayesian reliability assurance tests such as the Bayesian assurance test, the assurance reliability demonstration test, and the coverage criterion test. Efficient algorithms are presented to compute the set of test plans, providing practitioners with a comprehensive range of options to choose from. In addition, we include a comparison to the sequential probability ratio test. We also provide formal proofs for the inverse relationship between consumer's and producer's risk in Bayesian reliability assurance tests that underlie our algorithms. A case study is presented to illustrate the framework's application and compare the risks associated with different test plans.

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Planning reliability demonstration tests with limited expected risks

Cited by 6 publications

References 63 publications

A modified reliability acceptance sampling plan with warranty return rate consideration under Weibull‐distributed accelerated lifetime data

A modified reliability acceptance sampling plan with warranty return rate consideration under Weibull‐distributed accelerated lifetime data

Overdispersion Effects on Reliability Test Planning

Comparing Risks for Binomial Reliability Assurance Test Planning

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