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引用次数: 0
摘要
在可靠性理论中,以随机阶数对连贯系统进行比较至关重要。虽然有大量文献致力于比较具有同质独立组件的系统,但现实世界中的系统往往由异质组件组成。因此,本文旨在研究具有异构独立组件的系统,以及具有异构从属组件的系统。为此,我们考虑由三个组件组成的系统,这三个组件属于两种不同的组件类型,即两个 A 型组件和一个 B 型组件。当组件独立时,系统的寿命分布用故障特征来表示,故障特征是组件寿命分布的函数。然而,当组件相互依赖时,系统的寿命分布则使用协整和对角线部分来表示。此外,我们还利用扭曲分布来进行无分布随机比较。利用这些表示方法,我们在三种情况下比较了具有反向危险率比例成分的系统:(i) 当各组成部分是异质和独立时;(ii) 当各组成部分是异质和依赖时;最后,(iii) 将具有同质和独立组成部分的系统与具有异质组成部分的系统进行比较。为了说明这些结果的适用性,我们提供了一些示例和应用。
Comparisons of coherent systems with two types of heterogeneous components having proportional reversed hazard rates
The comparison of coherent systems in terms of stochastic orders is vital in reliability theory. While there is a considerable amount of literature devoted to comparing systems with homogeneous and independent components, real-world systems often consist of heterogeneous components. Hence, this article aims to investigate systems with heterogeneous and independent components, as well as, those with heterogeneous and dependent components. For this purpose, we consider systems comprise of three components, which are of two different types of components, namely two components of type A and one component of type B. The system's lifetime distribution is represented using the failure signature when the components are independent, which is a function of the component's life distribution. However, when the components are dependent, the system's lifetime distribution is represented using copula and diagonal sections. Additionally, distorted distributions are utilized to enable distribution-free stochastic comparisons. Using these representations, we compare systems with components having proportional reversed hazard rates, in three scenarios: (i) when components are heterogeneous and independent; (ii) when components are heterogeneous and dependent; and finally, (iii) comparing systems with homogeneous and independent components with those that have heterogeneous components. To illustrate the applicability of these results, we provide some examples and applications.
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.