基于ALK-MCS算法的航空发动机轴承可靠性分析

Reliability Analysis for Bearings of Aero-Engine Based on ALK-MCS Algorithm

作为航空发动机的关键部件,主轴圆柱滚子轴承的可靠性水平直接影响航空发动机性能的稳定性与服役的安全性,因此准确分析主轴圆柱滚子轴承的可靠性至关重要。然而,圆柱滚子轴承的失效行为复杂,其可靠性分析中的功能函数呈现高度非线性且无法给出显式表达,此时使用代理模型近似构建圆柱滚子轴承的功能函数是一种行之有效的方法。将蒙特卡洛模拟法(MCS)和Kriging代理模型相结合,提出了基于主动学习Kriging(ALK)的ALK-MCS算法,并将该算法应用于某型号航空发动机主轴圆柱滚子轴承的可靠性分析。首先,建立圆柱滚子轴承的三维模型,其次对圆柱滚子轴承进行有限元仿真,最后基于ALK-MCS算法对其进行可靠性分析。结果表明ALK-MCS算法计算效率高,显著减少了圆柱滚子轴承的仿真次数,所得到的可靠性分析结果可为实际工程中的决策者提供参考。

As one of the key parts of aero-engine,the reliability of cylindrical roller bearings affects the stability of aero-engine to some extent.Therefore,it is very important to study the reliability of cylindrical roller bearings of aero-engine.However,the complex limit state functions often appear in the reliability analysis of cylindrical roller bearings,such as highly nonlinear functions,and even some limit state functions are implicitly given.At this time,it is an effective method to use surrogate model to approximate the limit state function of cylindrical roller bearings.In this paper,by combining with Kriging surrogate model and the active learning(active learning Kriging-Monte Carlo simulation)ALK-MCS algorithm,the reliability of an aero-engine’s cylindrical roller bearings is studied.Firstly,the three-dimensional model of a cylindrical roller bearing is established,then the finite element simulation is performed,and finally the ALK-MCS-based reliability analysis of the cylindrical roller bearing is carried out.The results show that as an active learning reliability method with high efficiency,ALK-MCS algorithm can significantly reduce the iteration.Simultaneously,the reliability result of the cylindrical roller bearing through ALK-MCS algorithm provides some guidance for the decision-maker in actual engineering.