基于多源数据驱动的机体结构运维优化技术

doi:10.7527/S1000-6893.2025.32789

Abstract

Abstract:

To meet the predictive and condition-based maintenance requirements for the airframe structures of large passenger aircraft， a multi-source data fusion driven system for air route maintenance is developed. This system integrates engineering data from the model type development phase， including flight test measured loads， full-aircraft finite element internal forces， structural fatigue damage assessment， airworthiness limitation items， and their associated maintenance requirements. By employing methods such as data cleaning， sample reconstruction， data modeling， damage accumulation analysis， and maintenance customization optimization， the system enables comprehensive condition-based maintenance support. Through application to a specific domestic large passenger aircraft， it has resolved key challenges in data modeling accuracy and prediction efficiency for airframe structure maintenance. The system achieves rapid single-flight prediction of “flight parameters-loads-stress-damage” for critical wing regions. Based on comparison between theoretical damage per flight and actual damage values at 95% confidence level， preliminary estimates indicate that the repeat inspection interval for high-frequency eddy current inspections can be extended as follows： from 5 000 to 8 091 flight cycles for stringer hole edges at the skin-stringer splice detailed position of the lower wing panel， and from 5 500 to 8 900 flight cycles for skin hole edges.

Key words: data-driven, rapid prediction, line maintenance, large passenger aircraft, fatigue quality

CLC Number:

V215.5⁺2

Lingang ZHU, Puhui CHEN. Multi-source data driven optimization technology for airframe structural operation and maintenance[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(12): 232789.

Figures/Tables 20

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Table 2

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 3

Fatigue life Weibull distribution shape parameters

材料	$α$
铝合金	4.0
中强钢和钛合金	3.0
高强钢	2.2

Table 3

Table 4

Fig.12

Fig.13

Fig.14

Fig.15

Table 5

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大气数据	推力及起落架	飞行控制	姿态角	重量状态
高度	左发推力转速	襟翼（2）	俯仰角	总重量
空速	右发推力转速	缝翼（2）	滚转角	重心
马赫数	反推控制状态	平尾	侧滑角	左机翼燃油重量
总压	起落架收放状态	升降舵（2）	迎角	右机翼燃油重量
静压	起落架接地状态（3）	方向舵	俯仰角速率	中央翼燃油重量
总温	前轮转弯角度	副翼（2）	滚转角速率
	刹车控制指令	多功能扰流板（8）
		地面扰流板（2）
		侧杆指令（4）
		脚蹬指令（2）

建模方法	满足精度要求占比	训练时间	单架次预测时间/s	模型大小
多重回归	0.9	10 min	0.5	10 M
神经网络	0.92	2 h	5	3 G
随机森林	0.95	6 h	30	30 G
极端随机树	0.98	95 s	5	1.8 G

检查对象	初始检查间隔/架次		优化检查间隔/架次
蒙皮孔边	检查门槛	11 000	检查门槛	17 799
	高频涡流重复检查间隔	5 500	高频涡流重复检查间隔	8 900
	详细目视重复检查间隔		详细目视重复检查间隔
长桁孔边	检查门槛	9 900	检查门槛	16 019
	高频涡流重复检查间隔	5 000	高频涡流重复检查间隔	8 091
	详细目视重复检查间隔	1 800	详细目视重复检查间隔	2 913

Multi-source data driven optimization technology for airframe structural operation and maintenance

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材料	q
铝合金	4.0
钛合金	6.5
钢	7.5