基于多源异构信息的航空发动机转子参数校准与可靠性分析

doi:10.7527/S1000-6893.2023.28575

Abstract

Abstract:

The accuracy of reliability analysis results of complex systems is closely related to the accuracy of input parameters. A stochastic model correction and parameter calibration method based on Bayesian maximum entropy is proposed to solve the reliability analysis problem containing multi-source uncertain information. By converting multi-source statistical information （such as moment information and reliability） into constraint conditions， this method transforms parameter estimation into uncertainty optimization problem. Further considering the mixed uncertainty， Wasserstein distance is introduced to construct the likelihood function， and the approximation algorithm is used to improve the computational efficiency. This method extends the application scope of classical Bayesian inference by adding "entropy term" and can deal with multi-source heterogeneous data and mixed uncertainty problems. A multi-state system reliability model based on survival signature was established for a multi-component aero-engine rotor system， and the reliability analysis was carried out by using the above method. Through comparative analysis， it was verified that the proposed method has higher accuracy and stronger robustness than the traditional method.

Key words: reliability analysis, multi-source heterogeneous information, mixed uncertainty, multistate system, survival signature

CLC Number:

V215.7

Lechang YANG, Chenxing WANG. Parameter calibration and reliability analysis of an aero-engine rotor based on multi-source heterogeneous information[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(23): 228575-228575.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Parameter setting

组件名	参数	含义	值
涡轮盘（C₁₁、C₁₂、C₁₃）	p	经验系数	1.45×10¹²
	$σ m a x$ /MPa	最大应力	874
	n	经验系数	0.08
	K	经验系数	1 419
	q	经验系数	q ~ Gaussian （μ_q， σ_q ） $μ q ∈ [- 0.4, - 0.2], σ q ∈ [0,0.1]$
轴承（C₂₁、C₂₂、C₂₃）	k	经验系数	k ~ Gaussian （μ_k， σ_k ） $μ k ∈ [0.2,2], σ k ∈ [0,0.1]$
	F	载荷	20N
	v	滑移速度	v ~ Gaussian （μ_v， σ_v ） μ_v = 0.1， σ_v = 0.02
涡轮叶片（C₃₁、C₃₂、C₃₃）	λ₂₁	状态转移速率	3×10^-5
涡轮叶片（C₃₁、C₃₂、C₃₃）	λ₁₀	状态转移速率	5×10^-5
涡轮轴（C₄）	α	尺度参数	6×10⁵
涡轮轴（C₄）	β	形状参数	4.0

Table 1

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table A1

Survival signature of system

l₁		l₂		l₃			l₄		$Φ H = h (l 1,0, …, l 4,2)$
l₁₀	l₁₂	l₂₀	l₂₂	l₃₀	l₃₁	l₃₂	l₄₀	l₄₂	h=0	h=1	h=2
［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	1	0	1	0	0
3	0	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	0	1	1	0	0
［0，1，2，3］	［0，1，2，3］	3	0	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	0	1	1	0	0
［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	［0，1，2，3］	3	0	0	0	1	1	0	0
［1，2］	［1，2］	［1，2］	［1，2］	2	1	0	0	1	8/9	1/9	0
［1，2］	［1，2］	［1，2］	［1，2］	2	0	1	0	1	8/9	0	1/9
［1，2］	［1，2］	［1，2］	［1，2］	1	2	0	0	1	8/9	1/9	0
［1，2］	［1，2］	［1，2］	［1，2］	1	1	1	0	1	8/9	1/18	1/18
［1，2］	［1，2］	［1，2］	［1，2］	1	0	2	0	1	8/9	0	1/9
［1，2］	［1，2］	［1，2］	［1，2］	0	3	0	0	1	2/3	1/3	0
［1，2］	［1，2］	［1，2］	［1，2］	0	2	1	0	1	7/9	1/9	1/9
［1，2］	［1，2］	［1，2］	［1，2］	0	1	2	0	1	7/9	1/9	1/9
［1，2］	［1，2］	［1，2］	［1，2］	0	0	3	0	1	2/3	0	1/3
［1，2］	［1，2］	0	3	2	1	0	0	1	2/3	1/3	0
［1，2］	［1，2］	0	3	2	0	1	0	1	2/3	0	1/3
［1，2］	［1，2］	0	3	1	2	0	0	1	2/3	1/3	0
［1，2］	［1，2］	0	3	1	1	1	0	1	2/3	1/6	1/6
［1，2］	［1，2］	0	3	1	0	2	0	1	2/3	0	1/3
［1，2］	［1，2］	0	3	0	3	0	0	1	0	1	0

Table A1

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Parameter calibration and reliability analysis of an aero-engine rotor based on multi-source heterogeneous information

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