Optimal durations of Weibull reliability tests based on failure counts

Fernández, Arturo J.

doi:10.1016/j.cie.2021.107247

Cited by 7 publications

(4 citation statements)

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“…In this case, T has a Weibull W (s, λ) distribution with parameters s and λ. In practice, the Weibull shape parameter s is often tied to the device failure mechanism [25]. If S t = (n, k) is a Weibull RDT plan, the corresponding OC function can be expressed as…”

Section: Influence Of the Overdispersionmentioning

confidence: 99%

“…In order to solve the optimization problem (9) for Weibull, gamma and lognormal lifetime distributions, we refer to the corresponding computational methods proposed by Fernández [25], [51], [58] in the binomial case.…”

Section: Minimize Cmentioning

confidence: 99%

“…Thus, as the failure rate is proportional to the test time when s = 2, the engineer intrinsically assumes that the wear-out of the chips is linear. According to Fernández [25] the choice of the Weibull shape parameter s is usually based on previous data, expert opinions, and available knowledge of the underlying failure mechanism. In general, the RDT plans are quite robust to slight changes in s.…”

Section: A Checking Of Microelectronic Chipsmentioning

confidence: 99%

“…If t denotes the test time, n represents the number of devices randomly selected from the submitted lot and X n,t designates the number of observed failures at time t, then the entire lot is accepted if and only if (iff) X n,t is at most the so-called acceptance number, k. Hence, a t-duration reliability test plan is described by the pair (n, k). Recently, Fernández [25] has proposed lot inspection schemes with optimal test times by minimizing cost functions. In this case, a reliability test plan is characterized by the triple (n, k, t).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Influence Of the Overdispersionmentioning

confidence: 99%