This paper is concerned with system reliability improvement at as early as the system's embodiment design stage. Design decisions made at this stage, including component reliability specifications, will have long-term impacts on the entire lifecycle of the system, but they are also difficult to make because the knowledge of system and/or subsystem performance is most likely incomplete at this moment. Thus the system reliability function cannot be fully defined analytically. Because of this inherent uncertainty at early design stage, the traditional reliability evaluation methods, which assume deterministic reliability-wise structures and component reliability specifications, may not be appropriate in real-world applications. In this paper, a Nonparametric Bayesian Network (NPBN) approach is proposed to tackle such challenges at early design stage. The proposed method can represent the uncertainty in the structure of system reliability by combining any available information of component performance in some similar or past-generation systems. This approach can partially quantify the reliability function of the system under design and propose design change recommendations in order to achieve the targeted system reliability level.

中文翻译：

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在可靠性结构的不确定性下改进早期系统设计

本文早在系统的实施例设计阶段就关注系统可靠性的改进。在这个阶段做出的设计决策，包括组件可靠性规范，将对系统的整个生命周期产生长期影响，但它们也很难做出，因为此时系统和/或子系统性能的知识很可能是不完整的. 因此，系统可靠性函数不能完全解析定义。由于早期设计阶段的这种固有不确定性，传统的可靠性评估方法，假设确定性的可靠性结构和组件可靠性规范，可能不适用于实际应用。在本文中，提出了一种非参数贝叶斯网络 (NPBN) 方法来解决早期设计阶段的此类挑战。所提出的方法可以通过结合一些类似或过去一代系统中组件性能的任何可用信息来表示系统可靠性结构中的不确定性。这种方法可以部分量化设计系统的可靠性函数，并提出设计变更建议，以达到目标系统的可靠性水平。