可靠性寿命高效分析方法及在涡轮轴中的应用

doi:10.7527/S1000-6893.2025.32111

Abstract

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

CLC Number:

V215.7

Yixin LU, Zhenzhou LYU, Hengchao LI. Efficient analysis method of reliability lifetime and its application in turbine shaft[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(2): 232111.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Table 1

Calculation results of test functions

函数	方法	t_s/s	$δ R E$ /%	调用次数N_call	耗时/s
函数1	D-MCS	0.100 2		6×10¹⁰	3 356.83
	SLAS	0.100 2	0	163	81 237.16
	KFFI	0.100 2	0	158	77 896.92
	TP-SS-AK	0.103 2	2.99	107	9 381.23
	所提方法	0.098 7	1.50	89	314.73
函数2	D-MCS	0.121 2		10¹³	2.48×10⁶
	TP-SS-AK	0.125 4	3.47	1 176	14 305.33
	所提方法	0.118 8	1.98	523	973.16

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Table 2

Fig.8

Table 3

Table 4

Reliability lifetime calculation results of turbine shaft structure

目标失效概率	方法	t_s/月	N_call	耗时/天
$P f * = 7 × 10 - 3$	SLAS	4.14	453	37.47
	KFFI	4.14	430	35.55
	TP-SS-AK	4.14	295	5.93
	所提方法	4.14	271	3.48
$P f * = 2 × 10 - 6$	TP-SS-AK	1.93	367	9.43
$P f * = 2 × 10 - 6$	所提方法	1.93	308	5.74

Table 4

Fig.9

Fig.10

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参数	数值/型号
通气孔半径/mm	0.75
花键间距/mm	3.9
花键内径/mm	39
材料	GH4169
弹性模量/GPa	146
泊松比	0.325

输入变量	分布类型	参数1	参数2	自相关函数
d/mm	正态分布	1.50	0.015
h/mm	均匀分布	3.90	4.10
D/mm	正态分布	39	0.76
ω/（rad·s^-1）	正态分布	1 124	33.72
E/GPa	正态分布	146	4.38
ν	极值分布	0.325	0.006 5
T（t）/ （N·m）	高斯型随机过程	1 800	54	exp［-（t₁- t₂）²/20］

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

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