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REDUCTION OF SYSTEM RELIABILITY MODEL UNCERTAINTY BY CONSIDERING DEPENDENT COMPONENTS WITH STOCHASTIC PROCESS LOADING

机译：通过考虑随机处理加载的依赖性组件来减少系统可靠性模型的不确定性

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When component dependence is ignored, a system reliability model may have large model (epistemic) uncertainty with wide reliability bounds. This makes decision making difficult during the system design. Component dependence exists due to the shared environment and operation conditions. It is difficult for system designers to model component dependence because they may not have access to component design details if the components are designed and manufactured by outside suppliers. This research intends to reduce the system reliability model uncertainty with a new way for system designers to consider the component dependence implicitly and automatically without knowing component design details. The proposed method is applicable for a wide range of applications where the time-dependent system stochastic load is shared by components of the system. Simulation is used to obtain the extreme value of the system load for a given period of time, and optimization is employed to estimate the system reliability interval. As a result, the epistemic uncertainty in system reliability can be reduced.
机译：当忽略组件依赖性时，系统可靠性模型可能具有大型模型（认识）不确定度，具有宽的可靠性范围。这在系统设计期间做出了困难的决策。由于共享环境和操作条件，存在组件依赖性。系统设计人员很难模拟组件依赖性，因为如果组件由外部供应商设计和制造，它们可能无法访问组件设计细节。本研究旨在通过新的方式来减少系统可靠性模型的不确定性，用于系统设计人员隐式和自动地考虑组件依赖性而不知道组件设计细节。所提出的方法适用于各种应用的应用，其中系统的组件共用了时间依赖的系统随机负荷。模拟用于获得给定时间段的系统负载的极值，并且采用优化来估计系统可靠性间隔。结果，可以减少系统可靠性的认知不确定性。

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