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机构地区:[1]东北大学机械工程与自动化学院,沈阳110004
出 处:《机械强度》2005年第4期492-497,共6页Journal of Mechanical Strength
基 金:国家自然科学基金资助项目(50275025)~~
摘 要:研究一般串联、并联及KN表决系统(不要求系统中各零件失效事件是相互独立的)的失效概率建模与失效概率预测。通过详细考察、深入分析应力—强度干涉理论、方法及其在零件失效概率和系统失效概率计算中的建模过程,介绍“系统级”的应力—强度干涉模型及其在系统失效概率分析中的应用,并给出应用实例及其与实验观测结果的比较。与传统的先计算零件失效概率,再在各零件失效相互独立的(不真实)假设条件下建立串联、并联、表决等系统失效概率模型的方法不同,文中提出应用“系统级”的应力—强度干涉分析直接建立系统失效概率模型的方法。为应用应力—强度干涉模型预测系统失效概率,首先详细分析传统的零件失效干涉分析过程中发生的系统可靠性信息损失问题。分析表明,零件失效概率的不确定性与载荷分散程度有关,在传统的应用应力—强度干涉模型计算零件失效概率过程中遗失的失效相关性信息对系统失效概率估算是十分重要的。在此基础上,指出无法通过传统的零件可靠度参数构建一般系统(具有失效相关性的非独立失效系统)可靠性模型的原因。最后,建立能根据系统低阶失效数据预测高阶失效概率的参数化模型。Aiming at modeling and assessing failure probability of general series, parallel, or k-out-of-n redundant system in which no premise of “component failure events being s-independent of each other” is required, the present paper investigated the loadstrength interference theory and its application in component/system failure probabihty estimation as well. Through a thoroughly analysis to the load-strength interference theory and the process of calculating component failure probabihty by this theory, the present paper introduced a “system-level” load-strength interference method to system reliabihty modeling. Examples were presented to show the methodology and its agreement with experimental results. Different from the conventional system reliability estimation method by which system rehability was calculated directly from component rehability under the assumption of independent failure among components, the proposed method was applying load-strength interference relationship at system-level and calculated system failure probability according to load distribution and strength distribution. As a basis of developing the methodology, the phenomenon of information loss in the process of conventional component reliability analysis and modeling was discussed, and the reason that why it is impossible to construct dependent system rehability model simply by means of component rehability parameter was declared. Finally, a parametric model was developed to predict high multiple failure probability according to low multiple failure event data of the associated system.
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