为保证航空结构的安全服役，十分有必要分析极小目标失效概率约束下的可靠性寿命。然而，现有的分析方法的计算效率均难以满足工程中高可靠性要求下寿命分析需求。为此，提出了一种基于首次失效时刻的序列分层重要抽样可靠性寿命分析方法。首先，建立了序列分层探索极小目标失效概率对应的稀有失效域的策略，该策略将稀有失效域的探索问题转换为了逐步探索一系列概率从大到小的失效域问题，有效地降低获取稀有失效域信息的难度。其次，提出了分层构建显式规则重要抽样密度函数的方法，降低了稀有失效域内重要抽样样本获取的难度和计算量，提升了可靠性寿命分析的计算效率。此外，为减小时变功能函数的调用次数，将Kriging代理模型嵌入提出的序列分层重要抽样方法中，并设计首次失效时刻误判引导的自适应更新策略，增强了序列分层重要抽样方法求解极小目标失效概率约束下的可靠性寿命的效率。结果表明，对于测试函数，所提方法与现有先进方法相比在功能函数调用次数和计算耗时分别最多减少了45.4%和99.6%；对于某型航空发动机涡轮轴结构，所提方法与现有先进方法相比在功能函数调用次数和计算耗时分别最多减少了40.2%和90.7%。
In order to ensure the safe service of aero structure, it is of great significance to evaluate the reliability lifetime under the constraint of extremely small target failure probability. However, the computational efficiency of the existing reliability lifetime analysis methods is difficult to meet the requirements of reliability lifetime analysis under high reliability requirements in engineering effectively. For this issue, a sequential stratified importance sampling method based on the first failure instant is proposed to solve reliability lifetime in this paper. First, a sequential stratified exploration strategy for the rare failure domain with the extremely small target failure probability is established, which transforms the exploration problem of the rare failure domain into a gradual exploration problem of a series of failure domains with large probabilities, and it can effectively reduce the difficulty of obtaining the rare failure domain information. Second, hierarchically constructing the explicit rule importance sampling density function is proposed to reduces the difficulty and computational complexity of obtaining the importance sample in the rare failure domain, which improves the computational efficiency for solving the reliability lifetime. In addition, in order to reduce the number of model evaluations, the Kriging surrogate model is embedded into the proposed sequential stratified importance sampling method, and an adaptive update strategy guided by misjudgment of the first failure instant is designed, which improve the efficiency of the sequential stratified importance sampling method to solve the reliability lifetime under the constraint of the extremely small target failure probability. The results show that for the test function, the proposed method reduces the number of model evaluations and computational time by up to 45.4% and 99.6%, respectively, compared with the state-of-the-art methods. For a certain type of aero-engine turbine shaft structure, the proposed method reduces the number of model evaluations and computational time by up to 40.2% and 90.7%, respectively, compared with the state-of-the-art methods.
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