دانلود مقاله ISI انگلیسی شماره 113042
ترجمه فارسی عنوان مقاله

تجزیه و تحلیل شکست های احتمالی در سیستم های مأموریت مرحله ای

عنوان انگلیسی
Probabilistic competing failure analysis in phased-mission systems
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
113042 2018 38 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Reliability Engineering & System Safety, Volume 176, August 2018, Pages 37-51

ترجمه کلمات کلیدی
سیستم ماموریت مرحله ای، اثر رقابت احتمالی، مدل سازی قابلیت اطمینان، شبکه بی سیم بدن،
کلمات کلیدی انگلیسی
Phased-mission system; Probabilistic competing effect; Reliability modeling; Wireless body area network;
پیش نمایش مقاله
پیش نمایش مقاله  تجزیه و تحلیل شکست های احتمالی در سیستم های مأموریت مرحله ای

چکیده انگلیسی

Many real-world systems are classified as phased-mission systems (PMSs). These systems involve multiple, consecutive, non-overlapping phases of operations, where system configuration and component behavior can vary from phase to phase due to changing tasks and environmental conditions. In addition, statistical dependencies exist across phases for a given component. These dynamic, dependent behaviors make reliability analysis of PMSs more challenging than single-phase systems. Further complicating the PMS analysis is the probabilistic functional dependence behavior where operations of some system components (referred to as probabilistic-dependent components) rely on functions of other components (referred to as trigger components) with certain probabilities. Time-domain competitions exist between a trigger component failure and propagated failures of related probabilistic-dependent components; different occurrence sequences can cause distinct system statuses. This paper models effects of phase-dependent, probabilistic competing failures, and suggests a multiple-valued decision diagram-based combinatorial procedure for reliability analysis of non-repairable PMSs. The method is applicable to arbitrary types of time-to-failure distributions for system components and probabilistic isolation factors, as well as different statistical relationships between local and propagated failures of the same component. A case study is presented to illustrate applications and advantages of the proposed method. Correctness of the method is verified using Monte Carlo simulations.