Special Issue: Aircraft Digital Twin Technology

Twinning technology of key part load based on flight parameters

  • Liang CHEN ,
  • Lei HUANG ,
  • Yuxuan GU ,
  • Cong GUO ,
  • Kexin LIN ,
  • Yu GUAN ,
  • Jian SONG
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  • 1.AVIC Shenyang Aircraft Design and Research Institute,Shenyang 110000,China
    2.Key Laboratory of Digital Twin for Aircraft Structural Strength in Liaoning Province,Shenyang 110000,China
    3.School of Mechanics and Aerospace Engineering,Dalian University of Technology,Dalina 116024,China
E-mail: d20152303@163.com

Received date: 2024-09-30

  Revised date: 2024-10-24

  Accepted date: 2024-12-11

  Online published: 2024-12-23

Supported by

National Defense Basic Scientific Research Program(JCKY2021205B003)

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.

Cite this article

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 AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(19) : 531292 -531292 . DOI: 10.7527/S1000-6893.2024.31292

References

[1] 朱亮, 雷晓欣, 李小鹏, 等. 加改装飞机局部结构载荷谱实测与数据处理方法研究[J]. 航空科学技术202233(6): 46-52.
  ZHU L, LEI X X, LI X P, et al. Research on load measurement and data processing method of local structure of modified aircraft[J]. Aeronautical Science & Technology202233(6): 46-52 (in Chinese).
[2] OU J P, LI H. Structural health monitoring in mainland China: Review and future trends[J]. Structural Health Monitoring20109(3): 219-231.
[3] 王彬文, 肖迎春, 白生宝, 等. 飞机结构健康监测与管理技术研究进展和展望[J]. 航空制造技术202265(3): 30-41.
  WANG B W, XIAO Y C, BAI S B, et al. Research progress and prospect of aircraft structural health monitoring and management technology[J]. Aeronautical Manufacturing Technology202265(3): 30-41 (in Chinese).
[4] MOLENT L, AKTEPE B. Review of fatigue monitoring of agile military aircraft[J]. Fatigue & Fracture of Engineering Materials & Structures200023(9): 767-785.
[5] 庄存波, 刘检华, 熊辉, 等. 产品数字孪生体的内涵、体系结构及其发展趋势[J]. 计算机集成制造系统201723(4): 753-768.
  ZHUANG C B, LIU J H, XIONG H, et al. Connotation, architecture and trends of product digital twin[J]. Computer Integrated Manufacturing Systems201723(4): 753-768 (in Chinese).
[6] 张彦军, 王斌团, 宁宇, 等. 基于健康监测的飞机结构寿命预测技术[J]. 航空工程进展202415(1): 1-14.
  ZHANG Y J, WANG B T, NING Y, et al. Life prediction technology of aircraft structures based on structural health monitoring[J]. Advances in Aeronautical Science and Engineering202415(1): 1-14 (in Chinese).
[7] 郭丞皓, 于劲松, 宋悦, 等. 基于数字孪生的飞机起落架健康管理技术[J]. 航空学报202344(11): 227629.
  GUO C H, YU J S, SONG Y, et al. Application of digital twin-based aircraft landing gear health management technology[J]. Acta Aeronautica et Astronautica Sinica202344(11): 227629 (in Chinese).
[8] 李文龙, 杨美娟, 唐宁, 等. 某型飞机关键部位结构应变预测[J]. 应用力学学报202138(2): 649-654.
  LI W L, YANG M J, TANG N, et al. Structural strain prediction of key parts of an aircraft[J]. Chinese Journal of Applied Mechanics202138(2): 649-654 (in Chinese).
[9] 薛海峰, 宁宇, 张彦军, 等. 一种飞参应变预测模型的多参数优化方法: CN114282307A[P]. 2022-04-05.
  Xue H F, Ning Y, Zhang Y J, et al. A multi-parameter optimization method for a strain prediction model of flight parameters: CN114282307A[P]. 2022-04-05 (in Chinese).
[10] 中国人民解放军总装备部军事训练教材编辑工作委员会. 飞行器系统辨识学[M]. 北京: 国防工业出版社, 2003:345-367.
  CAI J S. Aircraft system identification [M]. Beijing: National Defense Industry Press, 2003: 345-367 (in Chinese).
[11] 马明, 李启明, 丁玲. 基于激光跟踪仪的民用飞机风标式攻角传感器零位误差测量数据分析[J]. 科技视界2018(18): 24-26.
  MA M, LI Q M, DING L. Analysis of zero error measurement data of wind-vane angle-of-attack sensor of commercial airplane based on laser tracker[J]. Science & Technology Vision2018(18): 24-26 (in Chinese).
[12] ZHANG P X, WANG H Y, SHI Z S. Registration algorithm of time and sensor system errors for multi-platform[J]. Advanced Materials Research2012468-471: 2832-2835.
[13] 李富刚, 张聪, 田福礼, 等. 飞行数据相容性检验方法[J]. 航空工程进展20178(4): 479-485.
  LI F G, ZHANG C, TIAN F L, et al. Method for flight data compatibility analysis[J]. Advances in Aeronautical Science and Engineering20178(4): 479-485 (in Chinese).
[14] 叶子豪, 张晓敏, 刘亚飞, 等. 用于飞机模型参数辨识的飞行数据处理方法研究[J]. 航空科学技术202233(8): 78-87.
  YE Z H, ZHANG X M, LIU Y F, et al. Research on flight data processing method applied to parameters identification of aircraft model[J]. Aeronautical Science & Technology202233(8): 78-87 (in Chinese).
[15] 刘超, 刘庆, 田福礼. 用于气动导数辨识的试飞数据处理方法研究[J]. 航空工程进展20145(2): 187-192.
  LIU C, LIU Q, TIAN F L. Research on flight test data processing method applied to the identification of aerodynamic derivatives[J]. Advances in Aeronautical Science and Engineering20145(2): 187-192 (in Chinese).
[16] 兑红娜, 王勇军, 董江, 等. 基于飞行参数的飞机结构载荷最优回归模型[J]. 航空学报201839(11): 222167.
  DUI H N, WANG Y J, DONG J, et al. Optimal regression model for aircraft structural load based on flight data[J]. Acta Aeronautica et Astronautica Sinica201839(11): 222167 (in Chinese).
[17] MCCOLLOM N N, BROWN E R. PHM on the F-35 fighter[C]∥2011 IEEE Conference on Prognostics and Health Management. Piscataway: IEEE Press, 2011: 1-10.
[18] TIKKA J, SALONEM T. Parameter based fatigue life analysis for F-18 aircraft[C]∥24th ICAF Symposium. Amsterdam: ICAF, 2007.
[19] CHEN T Q, GUESTRIN C. XGBoost: A scalable tree boosting system[C]∥Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 785-794.
[20] 张成龙, 刘杰. 基于梯度提升决策树的轴承剩余使用寿命预测方法[J]. 信息与电脑(理论版)202032(10): 34-35.
  ZHANG C L, LIU J. Prediction method of bearing remaining useful life based on gradient boosting decision tree[J]. China Computer & Communication202032(10): 34-35 (in Chinese).
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