基于诊断与预测的金属搭接结构动态风险评估

doi:10.7527/S1000-6893.2025.32500

Abstract

Abstract:

Hidden cracks in metallic lap-joint structures are difficult to detect， and conventional risk assessment methods rely heavily on offline nondestructive inspections. To address these challenges， an integrated diagnosis and prognosis dynamic risk assessment method within a digital twin framework has been proposed. The method employs an experimental strain driven physics and data fusion crack propagation model and implements a dynamic Bayesian network to close the loop between virtual and physical models. Crack initiation is detected using the Cumulative Sum Control Chart （CUSUM） algorithm； crack location is determined by a k-Nearest Neighbors （KNN） classifier； crack size is estimated through a Multi-Output Gaussian Process Regression （MOGPR） model； subsequently， a Dynamic Bayesian Network （DBN） is employed to dynamically update the crack propagation parameters C and m in real time. Based on these updated parameters， the Single Flight Probability of Failure （SFPOF） is computed through Monte Carlo simulation. Experimental results demonstrate that the combined CUSUM and SFPOF criterion provides timely crack warnings and reduces dependence on high-precision Equivalent Initial Flaw Size （EIFS）. As monitoring data accumulate， the standard deviations of C and m decrease， lowering uncertainty in life predictions. The proposed method enables continuous online quantitative risk evaluation of metallic lap-joint structures， offering reliable support for condition-based maintenance decisions.

Key words: risk assessment, structural fault diagnosis and health management, fatigue damage diagnosis and prognosis, single flight probability of failure, crack propagation

CLC Number:

V215.5

Liang HAN, Xiaofan HE. Dynamic risk assessment of metallic lap-joint structures based on diagnosis and prognosis[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(7): 232500.

Figures/Tables 32

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Table 1

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Table 2

Optimized parameter configurations for KNN-bolt A and KNN-bolt B

参数	KNN-螺栓A	KNN-螺栓B
邻居数量	7	1
距离度量	余弦	余弦
距离权重	平方倒数	等权
平局处理	最小类别	最小类别
搜索方法	穷尽搜索	穷尽搜索
样本数量	26	26
分类成本	$0120$	$0120$

Table 2

Table 3

MOGPR model hyperparameter configurations

超参数	符号	MOGPR-螺栓A	MOGPR-螺栓B
核函数类型		MaternKernel	MaternKernel
核平滑参数	$ν$	1.5	1.5
输出尺度	$σ f 2$	0.699 5	1.099 9
长度尺度	$l$	13.702 2	13.722 4
噪声方差	$σ n 2$	0.000 135	0.000 136 4
任务数量		2	2
任务协方差矩阵	$B$	$13.449 9 1.479 4 1.479 4 0.447 1$	$8.590 9 0.945 2 0.945 2 0.285 0$
学习率		0.05	0.05
训练轮数		500	500

Table 3

Table 4

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Table 5

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Table 6

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试件	载荷条件	裂纹位置	断口图	a-N曲线
#1	F _max=10 kN，R=0.1	螺栓B	图10 （a）	图11 （a）
#2	F _max=10 kN，R=0.1	螺栓A	图10 （b）	图11 （b）
#3	F _max=8 kN，R=0.1	螺栓A	图10 （c）	图11 （c）
#4	F _max=8 kN，R=0.1	螺栓B	图10 （d）	图11 （d）

试件	检查周期/次	C （对数正态分布）		m （正态分布）
试件	检查周期/次	µ	σ	µ	σ
#1 峰值载荷10 kN	t ₀	-25.383 7	1.108 3	2.733 7	0.048 1
	t ₀+6 000	-25.648 3	0.952 1	2.729 0	0.047 1
	t ₀+12 000	-25.773 2	0.901 7	2.726 3	0.046 4
	t ₀+18 000	-25.823 9	0.845 2	2.723 1	0.045 2
	t ₀+24 000	-25.863 8	0.805 9	2.721 1	0.044 3
#3 峰值载荷8 kN	t ₀	-25.376 2	1.103 0	2.733 8	0.048 0
	t ₀+20 000	-25.829 4	0.889 9	2.726 8	0.046 2
	t ₀+40 000	-26.046 6	0.841 1	2.724 1	0.044 8
	t ₀+60 000	-26.197 3	0.781 8	2.723 6	0.043 9
	t ₀+80 000	-26.304 6	0.731 0	2.724 3	0.045 1

诊断间隔	时间/s
诊断间隔	#1	#2	#1和#2平均	#3	#4	#3和#4平均
ΔT = 250	978.36	1 083.72	1 031.04	2 253.22	1 356.02	1 804.62
ΔT = 500	746.67	660.54	703.605	1 113.78	689.26	901.52
ΔT = 1 000	339.7	406.08	372.89	585.67	373.09	479.38
ΔT = 2 000	110.13	294.58	202.355	314.05	180.51	247.28

Dynamic risk assessment of metallic lap-joint structures based on diagnosis and prognosis

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