数字孪生机翼损伤模式快速识别与监测方法

王子一; 粟华; 龚春林; 蔡艳芳; 丁轩鹤; 杨予成

doi:10.13224/j.cnki.jasp.20220395

数字孪生机翼损伤模式快速识别与监测方法

doi: 10.13224/j.cnki.jasp.20220395

王子一¹,
粟华^{1, 2,*, ,},
龚春林^{1, 2},
蔡艳芳³,
丁轩鹤¹,
杨予成¹

1.
西北工业大学航天学院，西安 710072
2.
西北工业大学空天飞行技术研究所，西安 710072
3.
西安现代控制研究所，西安 710065

基金项目: 基础科研计划（JCKY2020204B016）

详细信息

作者简介:
王子一（1998-），男，硕士生，主要从事飞行器总体设计研究

通讯作者:
粟华（1985-），男，副研究员，博士，主要从事多学科优化问题研究。E-mail：su@nwpu.edu.cn

中图分类号: V19
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- 文章访问数: 69
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- PDF量: 18
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-02
- 网络出版日期: 2023-11-27

Rapid identification and monitoring of digital twin wings damage patterns

WANG Ziyi¹,
SU Hua^{1, 2,*
, ,},
GONG Chunlin^{1, 2},
CAI Yanfang³,
DING Xuanhe¹,
YANG Yucheng¹

1.
School of Astronautics，Northwestern Polytechnical University，Xi’an 710072，China
2.
The Institute of Spaceplanes and Hypersonic Technology，Northwestern Polytechnical University，Xi’an 710072，China
3.
Xi’an Institute of Control Technology，Xi’an 710065，China

摘要

摘要:
针对飞行器结构健康监测过程中存在的识别流程复杂、实时性较差问题，提出一种基于数字孪生技术的飞行器机翼损伤模式识别与监测方法。采用模块化技术构建飞行器机翼的数字孪生结构模型，基于概率神经网络建立了传感器数据在结构数字孪生模型中的映射方法，形成了通用的数字孪生飞行器结构损伤模式快速识别流程。以某无人机为例，基于此流程方法建立了其机翼的损伤模式快速识别模型并开展了对损伤的识别。结果表明：构建的飞行器结构数字孪生识别模型对损伤模式的识别准确率达到了96%以上，能够实现动态航迹规划任务。
- 结构健康监测 /
- 数字孪生 /
- 损伤模式 /
- 模式识别 /
- 概率神经网络
Abstract:
To address the problems of complex recognition and poor real-time performance in the process of structural health monitoring of aircraft, a digital twin technology-based damage pattern recognition and prediction method for aircraft wings was proposed. The digital twin structural model of the aircraft wing was constructed using modular technology, and the mapping method of sensor data in the structural digital twin model was established based on probabilistic neural network, forming a fast monitoring process of general digital twin aircraft structural damage pattern. Based on an unmanned aerial vehicle, a rapid damage pattern recognition model of its wings was developed. The results showed that the damage pattern identification accuracy of the digital twin recognition model for aircraft structures reached over 96%, which could complete the dynamic trajectory planning task.
- structural health monitoring /
- digital twin /
- damage classifications /
- pattern recognition /
- probabilistic neural network

HTML

图 1 结构数字孪生飞行器损伤模式快速识别模型建模流程

Figure 1. Modeling process for rapid recognition of damage patterns in structural digital twin vehicles

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图 2 概率神经网络模型示意图

Figure 2. Schematic diagram of the probabilistic neural network model

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图 3 数据扩充与分类示意图

Figure 3. Schematic diagram of data expansion and classification

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图 4 飞行器几何布局及简单参数（单位：mm）

Figure 4. Geometric layout and simple parameters of the aircraft （unit：mm）

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图 5 飞行器损伤模式示意图

Figure 5. Schematic diagram of the damage state of the aircraft

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图 6 结构分析示意图

Figure 6. Schematic diagram of the structural analysis

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图 7 传感器位点布置示意图

Figure 7. Schematic diagram of sensor site layout

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图 8 测试数据点识别结果

Figure 8. Test data point identification results

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表 1 飞行器几何参数

Table 1. Geometry parameters of the aircraft mm

类型	参数	数值
机身	长度	1400
机身	厚度	70
机翼	单边翼展	1200
	翼根弦长	300
	翼梢弦长	300
	翼面最大厚度	14
	翼根最大翼厚长度	260
	翼梢最大翼厚长度	260
	单节隔框长度	200

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表 2 识别结果示意

Table 2. Identification results illustrated

识别模型偏移系数	识别为较为严重模式比例/%	识别为较为完好模式比例/%	识别准确率/%
0.2	15.7530	84.2470	98.0782
0.5	43.3841	56.6159	97.8848
0.8	73.8063	26.1937	96.3594

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表 3 识别结果差别分析

Table 3. Analysis of differences in identification results

识别不准确数据编号	识别损伤模式	实际损伤模式	损伤模式差别
1	第429种	第348种	第5翼梁实际损伤 50%，识别为0%
2	第534种	第615种	第5翼梁实际损伤 50%，识别为损伤100%
3	第555种	第393种	第5、第6翼梁实际损伤50%、50%，识别为损伤100%、0%

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表 4 识别模型抗干扰性能分析

Table 4. Analysis of the anti-interference performance of the recognition model

传感器精度/%	识别模型偏移系数	识别准确率/%
0.05	0.2	96.3539
	0.5	95.5809
	0.8	95.5008
0.2	0.2	90.7988
	0.5	88.5217
	0.8	87.2754
0.35	0.2	76.9342
	0.5	75.5339
	0.8	76.4658

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