基于飞行参数的结构关键部位载荷孪生技术

doi:10.7527/S1000-6893.2024.31292

Abstract

Abstract:

To effectively monitor the health of the aircraft， a twinning technology for load of key parts of the structure is proposed based on flying parameters in view of the complex load conditions during the service of the aircraft. Firstly， fiber optic sensor data optimization governance is completed using the methods such as adjacent value filling， one-/multi-dimensional data anomaly detection， wavelet packet decomposition and Bayesian threshold denoising. Secondly， based on the data mining method and linear regression method， methods are developed to extract the strain feature and related parameter of the key parts of the structure. Finally， the XGBoost model is used to train the mapping relationship between the relevant flight parameters and features and the strain of key parts， and a high precision twin mapping model of the flight parameters and strain is constructed. Using the flight parameters and sensor data as the original input， the high precision twin mapping model has an average prediction accuracy of above 95%， and can effectively and accurately monitor the healthy state of aircraft structure.

Key words: digital twin, flight data, structural strength, mapping model, health monitoring

CLC Number:

V215.2⁺7

Liang CHEN, Lei HUANG, Yuxuan GU, Cong GUO, Kexin LIN, Yu GUAN, Jian SONG. Twinning technology of key part load based on flight parameters[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531292.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Table 1

Characteristic of flight data

名称	公式	说明	名称	公式	说明
均值	$x ¯ = 1 N ∑ n = 1 N x n$	表征信号能量的静态分量和中心趋势	峰值因子	$C = x p x r m s = m a x x n 1 N ∑ n = 1 N x n 2$	表征峰值在波形中的极端程度
峰值	$x p = m a x (x n)$	表征信号冲击的瞬时强度	脉冲因子	$I = x p x ¯ = m a x x n 1 N ∑ n = 1 N x n$	表征信号中冲击信号占比
峰峰值	$x p p = x m i n - x m a x$	表征信号强度的变化	裕度因子	$L = x p x r = m a x x n 1 N ∑ n = 1 N x n 2$	表征信号中冲击信号占比
绝对平均值	$x ¯ = 1 N ∑ n = 1 N x n$	表征信号的总体水平和变化特征	波形因子	$W = x r m s x ¯ = 1 N ∑ n = 1 N x n 2 1 N ∑ n = 1 N x n$	表征信号中冲击信号占比
均方根值	$x r m s = 1 N ∑ n = 1 N x n 2$	表征信号振动能量的大小	斜度	$S = ∑ n = 1 N x n - x ¯ 3 N - 1 σ 4$	表征数据分布的偏斜程度
方根幅值	$x r = 1 N ∑ n = 1 N x n 2$	表征信号的振动强度	峭度	$K = ∑ n = 1 N x n - x ¯ 4 N - 1 σ 4$	表征波形的平缓程度
差值	$σ = 1 N ∑ n = 1 N (x n - x ¯) 2$	表征信号能量的动态分量

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 2

Fig.9

Table 3

Fig.10

Table 4

Fig.11

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飞行参数	相关性系数	飞行参数	相关性系数
A1	0.433	A24	0.097
A2	0.027	A25	0.008
A3	2.7×10^-3	A26	0.015
A4	0.145	A27	0.02
A5	0.038	A28	4.6×10^-3
A6	4.5×10^-3	A29	4.6×10^-3
A7	0.159	A30	8.4×10^-5
A8	0.159	A31	1.5×10^-3
A9	0.158	A32	0.387
A10	0.012	A33	4.4×10^-3
A11	0.01	A34	0.096
A12	0.159	A35	0.088
A13	0.159	A36	0.094
A14	0.158	A37	0.094
A15	0.176	A38	2.3×10^-3
A16	0.177	A39	0.028
A17	0.138	A40	0.053
A18	0.133	A41	0.03
A19	0.227	A42	0.011
A20	0.249	A43	4.6×10^-4
A21	0.015	A44	0.175
A22	0.165	A45	0.17
A23	0.263

输出	E_NRMSE/%
B1	2.74
B2	2.61
B3	2.57
B4	2.50
B5	2.97
B6	4.37
B7	2.82
B8	2.97
B9	2.89
B10	2.58
B11	2.58

输出	精度/%
输出	测试组2	测试组18	测试组20	测试组24
B1	4.06	4.11	2.92	3.83
B2	1.97	2.94	2.07	3.89
B3	2.95	3.32	2.46	3.71
B4	2.31	2.40	1.77	3.69
B5	2.29	2.72	2.51	3.95
B6	3.74	8.78	5.16	5.64
B7	2.36	2.13	1.79	4.04
B8	2.46	3.26	2.69	3.63
B9	2.60	3.28	1.93	4.11
B10	1.74	2.74	1.79	4.04
B11	3.92	3.29	2.94	3.58

Twinning technology of key part load based on flight parameters

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