基于分段线性函数柔性模型的负荷共享系统可靠性分析

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Shilpi Biswas, Ayon Ganguly, Debanjan Mitra
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引用次数: 0

摘要

用分段线性函数逼近构件寿命的累积危险函数,建立了一种分析荷载分担数据的灵活模型。所提出的模型是数据驱动的,不依赖于对底层组件生命周期的限制性参数假设。讨论了模型参数的极大似然估计和置信区间的构造。可靠性特性的估计,如在任务时间的可靠性,分位数函数,平均失效时间和平均剩余时间的负载共享系统在这种情况下发展。由于所提出的模型能够很好地拟合负载共享数据,因此也可以更好地估计这些重要的可靠性特征。通过详细的蒙特卡罗模拟研究表明,该模型的性能令人满意。对两个负载共享数据集的分析,一个与双电机负载共享系统的寿命有关,另一个与篮球比赛有关,提供了举例说明。总之,本文对可有效用于负载共享系统的灵活模型进行了全面的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliability Analysis of Load-Sharing Systems Using a Flexible Model With Piecewise Linear Functions

A flexible model for analysing load-sharing data is developed by approximating the cumulative hazard functions of component lifetimes by piecewise linear functions. The proposed model is data-driven and does not depend on restrictive parametric assumptions on underlying component lifetimes. Maximum likelihood estimation and construction of confidence intervals for model parameters are discussed. Estimates of reliability characteristics such as reliability at a mission time, quantile function, mean time to failure and mean residual time for load-sharing systems are developed in this setting. As the proposed model is capable of providing a good fit for load-sharing data, it also results in a better estimation of these important reliability characteristics. The performance of the proposed model is observed to be quite satisfactory through a detailed Monte Carlo simulation study. The analyses of two load-sharing datasets, one pertaining to the lives of two-motor load-sharing systems and another related to basketball games, are provided as illustrative examples. In summary, this article presents a comprehensive discussion on a flexible model that can be used for load-sharing systems efficiently.

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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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