Reliability and sensitivity analysis of relief valve mechanism of aircraft door considering wear
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摘要:
泄压阀机构是飞机舱门的重要组成部分，对维持飞机正常安全运行起到重要作用。在ADAMS软件中建立了飞机舱门泄压阀机构多体动力学仿真模型，研究销轴发生以磨损为主要形式的性能退化时，对泄压阀机构定位精度的影响。建立了泄压阀机构定位精度的功能函数，引入主动学习Kriging代理模型，挑选符合学习准则的点提高代理模型精度。采用Monte Carlo方法计算了不同磨损次数下泄压阀机构的失效概率与3种全局灵敏度指标的变化规律，并给出了不同阈值情况下失效概率和基于失效概率的全局灵敏度指标的变化规律。结果表明：在不同磨损次数下，对于磨损量较大的销轴，其全局灵敏度指标值也较大，并且各项指标随磨损次数的变化规律存在差异。
Abstract:As an important part of the aircraft door, the relief valve mechanism plays an important role in maintaining the normal and safe operation of the aircraft. In this paper, the multi-body dynamical model of the relief valve mechanism is built in ADAMS to study the effect of pin wear on positioning accuracy. The performance function of the positioning accuracy of the mechanism is established. The active Kriging model is introduced to find samples points which can obviously improve the fitting accuracy according to learning criteria. The Monte Carlo method is used to estimate failure probability and three sensitivity indices at different wear times, and the change rules of failure probability and failure probability based global sensitivity indices under different thresholds have been studied. The results show that under different wear times, the global sensitivity indices of a pin with a larger wear amount are also larger, and there are differences in the changes of various indices with the wear times.
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Key words:
- aircraft door /
- Kriging model /
- reliability /
- global sensitivity /
- motion accuracy
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图 1 飞机舱门泄压阀机构示意图
Figure 1. Schematic diagram of aircraft door's relief valve mechanism
图 2 不同磨损量下泄压阀的打开角度
Figure 2. Opening angles of relief valve under different wear amounts
图 3 不同销轴处的法向压力随磨损次数变化的曲线
Figure 3. Change of normal pressure with wear times for different pins
图 4 不同销轴处的磨损量随磨损次数变化的曲线
Figure 4. Change of wear amounts with wear times for different pins
图 5 泄压阀机构可靠性和灵敏度分析流程
Figure 5. Flowchart of reliability and sensitivity analysis for relief valve mechanism
图 7 不同磨损次数下失效概率随角度阈值的变化规律
Figure 7. Rule of failure probability varying with threshold under different wear times
图 10 给定阈值下εi随销轴磨损次数的变化规律
Figure 10. Rule of εi varying with pins wear times under fixed threshold
图 11 3×104次磨损时εi随角度阈值的变化规律
Figure 11. Rule of εi varying with angle threshold under 3×104 wear times
表 1 30 000次磨损时输入变量概率分布信息
Table 1. Probability distribution information of input variables at 30 000 times of wear
变量 分布类型 均值/mm 变异系数 X1 正态分布 0.255 0.1 X2 正态分布 0.065 0.1 X3 正态分布 0.074 0.1 X4 正态分布 0.101 0.1 X5 正态分布 0.117 0.1 
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