航空学报 > 2026, Vol. 47 Issue (2): 232111-232111   doi: 10.7527/S1000-6893.2025.32111

可靠性寿命高效分析方法及在涡轮轴中的应用

陆艺鑫1,2,3, 吕震宙1,2,3(), 李恒朝1,2,3   

  1. 1.西北工业大学 航空学院,西安 710072
    2.清洁高效透平动力装备全国重点实验室,西安 710072
    3.飞行器基础布局全国重点实验室,西安 710072
  • 收稿日期:2025-04-10 修回日期:2025-06-18 接受日期:2025-07-30 出版日期:2025-08-12 发布日期:2025-08-11
  • 通讯作者: 吕震宙 E-mail:zhenzhoulu@nwpu.edu.cn
  • 基金资助:
    国家自然科学基金(12572141)

Efficient analysis method of reliability lifetime and its application in turbine shaft

Yixin LU1,2,3, Zhenzhou LYU1,2,3(), Hengchao LI1,2,3   

  1. 1.School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China
    2.State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment,Xi’an 710072,China
    3.State Key Laboratory of Aircraft Configuration Design,Xi’an 710072,China
  • Received:2025-04-10 Revised:2025-06-18 Accepted:2025-07-30 Online:2025-08-12 Published:2025-08-11
  • Contact: Zhenzhou LYU E-mail:zhenzhoulu@nwpu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(12572141)

摘要:

为保证航空结构的安全服役,有必要分析极小目标失效概率约束下的可靠性寿命。然而,现有分析方法的计算效率难以满足工程中高可靠性要求下寿命分析需求。为此,提出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,将稀有失效域的探索问题转换为逐步探索一系列概率从大到小的失效域问题,降低获取稀有失效域信息的难度。然后,提出分层构建显式规则重要抽样密度函数的方法,降低稀有失效域内重要抽样样本获取的难度和计算量,提升可靠性寿命分析的计算效率。最后,为了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,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,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%。

关键词: 可靠性寿命, 可靠性分析, 重要抽样, 代理模型, 涡轮轴

Abstract:

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. Firstly, 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. Secondly, the method for hierarchically constructing the explicit rule importance sampling density function is proposed to reduce 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. Finally, 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.

Key words: reliability lifetime, reliability analysis, importance sampling, surrogate model, turbine shaft

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