阶跃应力加速降解试验首次通过分布的半参数评估

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lochana Palayangoda, Hon Keung Tony Ng, Ling Li
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引用次数: 0

摘要

在可靠性工程中,不同类型的加速降解试验被用来获取可靠性信息,以评估高可靠性或昂贵的产品。阶跃应力加速降解试验(SSADT)是一种有用的试验方案,可用于节省试验资源。受西安微电子研究所收集的运算放大器 SSADT 数据的启发,我们提出了一种无需严格分布假设的半参数 SSADT 数据分析方法。具体来说,我们提出了经验鞍点近似法,用于估算项目在 SSADT 实验中包含和不包含的应力水平下的寿命(首次通过时间)分布。蒙特卡罗模拟研究用于评估该方法的性能并说明其优势。最后,提出的半参数方法被用于分析激励数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semiparametric evaluation of first-passage distribution for step-stress accelerated degradation tests

In reliability engineering, different types of accelerated degradation tests have been used to obtain reliability information for evaluating highly reliable or expensive products. The step-stress accelerated degradation test (SSADT) is one of the useful experimental schemes that can be used to save the resources of an experiment. Motivated by the SSADT data for operational amplifiers collected in Xi'an Microelectronic Technology Institute, in which the underlying degradation mechanism of the operational amplifiers is unknown, we propose a semiparametric approach for SSADT data analysis that does not require strict distributional assumptions. Specifically, the empirical saddlepoint approximation method is proposed to estimate the items' lifetime (first-passage time) distribution at both stress levels included and not included in the SSADT experiment. Monte Carlo simulation studies are used to evaluate the performance and illustrate the advantages of the proposed approach. Finally, the proposed semiparametric approach is applied to analyze the motivating data set.

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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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