飞机结构完整性数智评估技术研究进展

Progress in research on digital-intelligent assessment technologies for aircraft structural integrity

摘要

飞机结构完整性一直是其安全服役的重要保障，其评估的实时性、有效性与前瞻性，是实现从异常/损伤感知到数智判读一体化转型的关键目标。本研究系统梳理飞机结构完整性评估的代际传承，阐明其从经验模型、计算机模拟仿真到数字化、智能化的发展历程。针对当前数智化转型的技术特点，提出了一种飞机结构完整性数智评估通用技术框架，涵盖感知、治理、算法与决策四大层级。重点研究了人工智能技术特别是机器学习、深度学习、数字孪生等在结构损伤识别、状态评估以及寿命预测中的应用进展。最后，深入剖析了多源数据治理、模型可解释性与复杂损伤耦合机理等关键挑战，并对未来基于数智融合的飞机结构完整性发展方向进行展望。

Abstract

Aircraft structural integrity constitutes the cornerstone of safe service. Enhancing the real-time capability，effectiveness，and predictiveness of its assessment is pivotal to realizing the integrated transformation from anomaly/damage sensing to intelligent interpretation. This paper systematically reviews the evolutionary lineage of aircraft structural integrity assessment，elucidating its progression from empirical modeling and computer simulation to the current stages of digitalization and intelligentization. Addressing the technical characteristics of the ongoing digital-intelligent transformation，a general technical framework for the digital-intelligent assessment of aircraft structural integrity is proposed. This framework encompasses four core layers:data sensing，data governance，model algorithms，and decision-making output. Furthermore，the paper focuses on reviewing the application advancements of artificial intelligence technologies-especially machine learning，deep learning，and digital twins-in structural damage identification，health status assessment，and residual life prediction. Last，key challenges such as multi-source data governance，model interpretability，and complex damage coupling mechanisms are analyzed in depth. The paper concludes by prospecting future development directions for aircraft structural integrity based on digital-intelligent fusion.

关键词

服役完整性 / 结构健康监测 / 数字孪生 / 人工智能 / 数据驱动

Key words

service integrity / structural health monitoring / digital twin / artificial intelligence / data-driven

参考文献

[1] 何宇廷, 张腾, 缑百勇. 基于作战完整性的军用飞机结构强度发展思考[J]. 航空工程进展, 2023， 14（5）: 1-7.

[2] HE Yuting， ZHANG Teng， GOU Baiyong. Opinions on the development of military aircraft structural strength based on aircraft structural operational integrity[J]. Advances in aeronautical science and engineering, 2023， 14（5）: 1-7（in Chinese）.

[3] 刘斌超, 鲁嵩嵩, 曾苇鹏, 等. 从金属材料疲劳性能的力学描述到飞机结构疲劳寿命评定:现状与展望[J]. 固体力学学报, 2023， 44（4）: 417-457.

[4] LIU Binchao， LU Songsong， ZENG Weipeng， et al. From mechanical description for metal fatigue properties to service life evaluation of aircraft structural components:status and challenges[J]. Chinese journal of solid mechanics, 2023， 44（4）: 417-457（in Chinese）.

[5] 何宇廷, 张腾, 崔荣洪. 基于作战效能的军用飞机维修与保障[J]. 空军工程大学学报（自然科学版）, 2019， 20（ 1）: 1-6.

[6] HE Yuting， ZHANG Teng， CUI Ronghong. Maintenance and support for military aircraft based on the combat effectiveness[J]. Journal of Air Force Engineering University（natural science edition）, 2019， 20（ 1）: 1-6（in Chinese）.

[7] TAVARES S M O， RIBEIRO J A， RIBEIRO B A， et al. Aircraft structural design and life-cycle assessment through digital twins[J]. Designs, 2024， 8（2）: 29.

[8] AI L， SOLTANGHARAEI V， BAYAT M， et al. Detection of impact on aircraft composite structure using machine learning techniques[J]. Measurement science and technology, 2021， 32（8）: 084013.

[9] CHEN G， SONG W R， SHAO W H， et al. Damage presence，localization，and quantification of aircraft structure based on end-to-end deep learning framework[J]. IEEE transactions on instrumentation and measurement, 2025， 74: 1-15.

[10] SESHADRI B R， KRISHNAMURTHY T. Structural health management of damaged aircraft structures using digital twin concept[C]// 25th AIAA/AHS Adaptive Structures Conference. Reston，VA，USA:AIAA， 2017:AIAA 2017-1675.

[11] HAFSI M. Potential of generative AI in knowledge-based predictive maintenance for aircraft engines[C]// Annual Conference of the PHM Society. College Park:The Prognostics and Health Management Society， 2025: 4352.

[12] 蒋祖国, 田丁栓, 周占廷. 飞机结构载荷/环境谱[M]. 北京: 电子工业出版社， 2012.

[13] 何宇廷. 飞行器服役（作战）完整性的提出与发展[J]. 航空工程进展, 2022， 13（3）: 1-11.

[14] HE Yuting. Presentation and development of aerocraft operational integrity[J]. Advances in aeronautical science and engineering, 2022， 13（3）: 1-11（in Chinese）.

[15] Military Standard. Aircraft structural integrity program:MIL-STD-1530A[S]. Washington D.C.:U.S. Air Force， 1975.

[16] Department of Defense Standard Practice. Aircraft structural integrity program（ASIP）:MIL-STD-1530C[S]. Washington D.C.:U.S. Department of Defense， 2005.

[17] 中国民用航空局. 咨询通告 AC 121-FS-2018-65-R1 航空器结构持续完整性大纲[S]. 北京: 飞行标准司， 2018.

[18] 崔德刚, 鲍蕊, 张睿, 等. 飞机结构疲劳与结构完整性发展综述[J]. 航空学报, 2021， 42（5）: 66-87.

[19] CUI Degang， BAO Rui， ZHANG Rui， et al. Development of aircraft structural fatigue and structural integrity:review[J]. Acta aeronautica et astronautica Sinica, 2021， 42（5）: 66-87（in Chinese）.

[20] 中国人民解放军总装备部. 军用飞机结构完整性大纲:GJB 775A-2012[S]. 北京: 总装备部军标出版发行部， 2012.

[21] 董聪, 郦正能. 结构系统可靠性初步评估方法[J]. 强度与环境, 1995， 22（2）: 60-65.

[22] DONG Cong， LI Zhengneng. An approximate evaluation method of srtuctural system reliability[J]. Structure & environment engineering, 1995， 22（2）: 60-65（in Chinese）.

[23] 诸德培. 飞机结构的可靠性和完整性[J]. 航空学报, 1986， 7（6）: 521-530.

[24] ZHU Depei. The reliability and the integrity of aircraft structures[J]. Acta aeronautica et astronautica Sinica, 1986， 7（6）: 521-530（in Chinese）.

[25] 黄旌, 卢钧, 毕大园. 基于有限元分析的机翼损伤评估的建模[J]. 长江大学学报（自科版）, 2006， 3（2）: 59-62.

[26] HUANG Jing， LU Jun， BI Dayuan. Modeling on airplane wing damage evaluation based on finite element analysis[J]. Journal of Yangtze University（natural science edition）, 2006， 3（2）: 59-62（in Chinese）.

[27] PILKEY W D. Curved bars[M]// PILKEY W D. Formulas for Stress，Strain，and Structural Matrices. Hoboken，NJ，USA:John Wiley & Sons，Inc.， 2004: 801-890.

[28] 施剑玮. 基于有限元法的复杂结构的损伤容限分析[J]. 科学技术与工程, 2012， 20（8）: 1965-1967.

[29] SHI Jianwei. Damage tolerance analysis of complicated structures based on FEA[J]. Science technology and engineering, 2012， 20（8）: 1965-1967（in Chinese）.

[30] FERRELL B L. Air vehicle prognostics and health management[C]// 2000 IEEE Aerospace Conference. Proceedings. Piscataway，NJ，USA:IEEE， 2000: 145-146.

[31] 栗琳. 数智维修导论[M]. 北京: 机械工业出版社， 2023.

[32] 黄蕾, 郭聪, 张小波, 等. 基于飞参-载荷-寿命数字孪生模型的飞机结构健康管理方法[J]. 航空学报, 2025， 46（21）: 150-168.

[33] HUANG Lei， GUO Cong， ZHANG Xiaobo， et al. Aircraft structural health management method based on flight parameter-load-life digital twin models[J]. Acta aeronautica et astronautica Sinica, 2025， 46（21）: 150-168（in Chinese）.

[34] 朱林刚, 陈普会. 基于多源数据驱动的机体结构运维优化技术研究[J/OL]. 航空学报. （2025-10-28）[2025-11-21]. https://link.cnki.net/urlid/11.1929.V.20251027.1344.002.

[35] ZHU Lingang， CHEN Puhui. Research on multi-source data-driven airframe structure operation and maintenance optimization technology[J/OL]. Acta aeronautica et astronautica Sinica. （2025-10-28）[2025-11-21]. https://link.cnki.net/urlid/11.1929.V.20251027.1344.002（in Chinese）.

[36] ZHANG J， LIN G， VAIDYA U， et al. Past，present and future prospective of global carbon fibre composite developments and applications[J]. Composites part B:engineering, 2023， 250: 110463.

[37] OSTACHOWICZ W， GÜEMES J A. New trends in structural health monitoring[M]. Vienna:Springer， 2013.

[38] 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, 2021， 38（2）: 649-654.

[39] LI Wenlong， YANG Meijuan， TANG Ning， et al. Structural strain prediction of key parts of an aircraft[J]. Chinese journal of applied mechanics, 2021， 38（2）: 649-654（in Chinese）.

[40] 穆腾飞, 李忠剑, 戴喜妹. 飞机结构健康监测技术[J]. 民用飞机设计与研究, 2020（3）: 35-41.

[41] MU Tengfei， LI Zhongjian， DAI Ximei. Aircraft structural health monitoring technology[J]. Civil aircraft design & research, 2020（3）: 35-41（in Chinese）.

[42] SPECKMANN H， HENRICH R. Structural health monitoring（SHM）-overview on technologies under development[C]// Proceedings of the 16th World Conference on NDT. 2004， 1-8.

[43] DI SANTE R. Fibre optic sensors for structural health monitoring of aircraft composite structures:recent advances and applications[J]. Sensors, 2015， 15（8）: 18666-18713.

[44] 孙见忠, 左洪福, 闫洪胜, 等. 民用飞机预测维修技术研究进展[J]. 航空科学技术, 2024， 35（7）: 14-31.

[45] SUN Jianzhong， ZUO Hongfu， YAN Hongsheng， et al. Research progress in predictive maintenance technology of civil aircraft[J]. Aeronautical science & technology, 2024， 35（7）: 14-31（in Chinese）.

[46] CHOI H S， CHO Y H. Structural health monitoring techniques for composite aircraft[J]. Journal of the Korean Society for Nondestructive Testing, 2010， 30（1）: 54-59.

[47] Boeing. Predictive maintenance-insight accelerator[EB/OL]. （ 2023-11-28）[2025-11-25]. https://services.boeing.com/flight-operations/flight-data-analytics/insight-accelerator.

[48] BERNARD W， HOFFMANN A. Skywise-big data platform as a foundation of airlines predictive and health monitoring[C]// Proceedings of the Asia Pacific Conference of the PHM Society 2023. College Park:The Prognostics and Health Management Society， 2023: 3722.

[49] 李磊, 李明宇, 赵昶升, 等. 飞机健康管理系统告警等级调整方法研究[J]. 航空维修与工程, 2019（11）: 50-53.

[50] LI Lei， LI Mingyu， ZHAO Changsheng， et al. Research on alert grade adjustment method of aircraft health management system[J]. Aviation maintenance & engineering, 2019（11）: 50-53（in Chinese）.

[51] AVCI O， ABDELJABER O， KIRANYAZ S， et al. A review of vibration-based damage detection in civil structures:from traditional methods to machine learning and deep learning applications[J]. Mechanical systems and signal processing, 2021， 147: 107077.

[52] 黄永, 鲍跃全, 李惠. 结构状态识别与评估的机器学习方法研究进展[J]. 力学进展, 2023， 53（4）: 774-792.

[53] HUANG Yong， BAO Yuequan， LI Hui. Research advances in machine learning for structural state identification and condition assessment[J]. Advances in mechanics, 2023， 53（4）: 774-792（in Chinese）.

[54] HE H X， YAN W M. Structural damage detection with wavelet support vector machine:introduction and applications[J]. Structural control and health monitoring, 2007， 14（1）: 162-176.

[55] ZHANG Z， ZHU E Y， WANG B， et al. Application and comparison of GRNN，BPNN and RBFNN in the prediction of suspender frequency and tension on arch bridge[J]. Journal of civil structural health monitoring, 2024， 14（8）: 1839-1855.

[56] GULGEC N S， TAKÁCˇ M， PAKZAD S N. Convolutional neural network approach for robust structural damage detection and localization[J]. Journal of computing in civil engineering, 2019， 33（3）: 04019005.

[57] 崔建国, 张善好, 于明月, 等. 基于GRNN-ELM的飞机复合材料结构损伤识别[J]. 南京航空航天大学学报, 2017， 49（4）: 468-473.

[58] CUI Jianguo， ZHANG Shanhao， YU Mingyue， et al. Damage identification of aircraft composite structure based on GRNN-ELM[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2017， 49（4）: 468-473（in Chinese）.

[59] RAHMAN T A Z. Parameters optimization of an SVM-classifier using stochastic fractal search algorithm for monitoring an aerospace structure[J]. International journal of fluids and heat transfer, 2016，1（1）: 69-79.

[60] DWORAKOWSKI Z， DRAGAN K， STEPINSKI T. Artificial neural network ensembles for fatigue damage detection in aircraft[J]. Journal of intelligent material systems and structures, 2017， 28（7）: 851-861.

[61] XU L， YUAN S F， CHEN J， et al. Guided wave-convolutional neural network based fatigue crack diagnosis of aircraft structures[J]. Sensors, 2019， 19（16）: 3567.

[62] 贾路, 曾昊, 彭靖波, 等. 基于数据增强与多尺度融合的航空发动机叶片损伤检测[J]. 航空动力学报, 2025， 40（10）: 241-253.

[63] JIA Lu， ZENG Hao， PENG Jingbo， et al. Aero-engine blade damage detection based on data augmentation and multi-scale fusion[J]. Journal of aerospace power, 2025， 40（10）: 241-253（in Chinese）.

[64] 毕得铨. 基于深度学习的航空发动机健康状态评估和剩余有效使用寿命研究[D]. 杭州: 杭州电子科技大学， 2025.

[65] 曹现刚, 段雍, 王国法, 等. 煤矿设备全寿命周期健康管理与智能维护研究综述[J]. 煤炭学报, 2025， 50（1）: 694-714.

[66] CAO Xiangang， DUAN Yong， WANG Guofa， et al. Research review on life-cycle health management and intelligent maintenance of coal mining equipment[J]. Journal of China Coal Society, 2025， 50（1）: 694-714（in Chinese）.

[67] PERINGAL A， MOHIUDDIN M B， HASSAN A. Remaining useful life prediction for aircraft engines using LSTM[EB/OL]. （ 2024-01-15）[2025-11-29]. https://arxiv.org/abs/2401.07590.

[68] LIU J Q， LEI F， PAN C L， et al. Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion[J]. Reliability engineering & system safety, 2021， 214: 107807.

[69] LIN L， TONG C S， FU S， et al. A prediction of crack propagation on aircraft wing via AK-TCN[J]. Engineering failure analysis, 2024， 164: 108693.

[70] ZHA W T， YE Y H. An aero-engine remaining useful life prediction model based on feature selection and the improved TCN[J]. Franklin open, 2024， 6: 100083.

[71] HUANG Z X， HE Y J， SICK B. Spatio-temporal attention graph neural network for remaining useful life prediction[C]// 2023 International Conference on Computational Science and Computational Intelligence（CSCI）. Piscataway，NJ，USA:IEEE， 2023: 99-105.

[72] NARWARIYA J， MALHOTRA P， TV V， et al. Graph neural networks for leveraging industrial equipment structure:an application to remaining useful life estimation[EB/OL]. （ 2020-06-30）[2025-11-27]. https://arxiv.org/abs/2006.16556.

[73] 董雷霆, 周轩, 赵福斌, 等. 飞机结构数字孪生关键建模仿真技术[J]. 航空学报, 2021， 42（3）: 107-135.

[74] DONG Leiting， ZHOU Xuan， ZHAO Fubin， et al. Key technologies for modeling and simulation of airframe digital twin[J]. Acta aeronautica et astronautica Sinica, 2021， 42（3）: 107-135（in Chinese）.

[75] 代定强, 周轩, 董雷霆, 等. 数字工程与数字孪生在航空疲劳与结构完整性领域的研究进展与展望[J]. 航空学报, 2025， 46（ 19）: 19-40.

[76] DAI Dingqiang， ZHOU Xuan， DONG Leiting， et al. Research progress and prospects of digital engineering and digital twin in field of aeronautical fatigue and structural integrity[J]. Acta aeronautica et astronautica Sinica, 2025， 46（ 19）: 19-40（in Chinese）.

[77] WANG T， LIU Z， LIAO M， et al. Life prediction for aircraft structure based on Bayesian inference:towards a digital twin ecosystem[C]// Proceedings of the Annual Conference of the PHM Society 2020. College Park:The Prognostics and Health Management Society， 2020: 1261.

[78] LAI X N， YANG L L， HE X W， et al. Digital twin-based structural health monitoring by combining measurement and computational data:an aircraft wing example[J]. Journal of manufacturing systems, 2023， 69: 76-90.

[79] WU Z H， LI J Z. A framework of dynamic data driven digital twin for complex engineering products:the example of aircraft engine health management[J]. Procedia manufacturing, 2021， 55: 139-146.

[80] 王海峰, 李嘉骏, 于凯, 等. 军用飞机敏捷研发数字化技术展望[J]. 航空工程进展, 2024， 15（6）: 1-12.

[81] WANG Haifeng， LI Jiajun， YU Kai， et al. The outlook for digital technologies in military aircraft agile development[J]. Advances in aeronautical science and engineering, 2024， 15（6）: 1-12（in Chinese）.

[82] 王海峰. 战斗机故障预测与健康管理技术应用的思考[J]. 航空科学技术, 2020， 31（7）: 3-11.

[83] WANG Haifeng. Research on application of prognostics and health management technology for fighter aircraft[J]. Aeronautical science & technology, 2020， 31（7）: 3-11（in Chinese）.

[84] 邹君, 刘佳鑫, 解江, 等. 复合材料壁板高能钝物冲击损伤特性数值研究[J/OL]. 应用力学学报. （2025-07-29）[2025-11-23]. https://link.cnki.net/urlid/61.1112.O3.20250729.1633.002.

[85] ZOU Jun， LIU Jiaxin， XIE Jiang， et al. Numerical study on the damage characteristics of composite panels under high-energy wide-area blunt impact[J/OL]. Chinese journal of applied mechanics. （2025-07-29）[2025-11-23]. https://link.cnki.net/urlid/61.1112.O3.20250729.1633.002（in Chinese）.

[86] 郭双喜, 李雪芹. 基于非线性弹簧界面的复合材料分层损伤预测[J]. 应用力学学报, 2019， 36（4）: 958-965.

[87] GUO Shuangxi， LI Xueqin. Delamination prediction in laminate composites based on nonlinear spring interface elements[J]. Chinese journal of applied mechanics, 2019， 36（4）: 958-965（in Chinese）.

[88] 景博, 黄崧琳, 王生龙, 等. 军用飞机PHM系统一体化设计架构分析[J]. 航空工程进展, 2022， 13（3）: 64-73.

[89] JING Bo， HUANG Songlin， WANG Shenglong， et al. Analysis on integrated design of military aircraft prognostic and health management（PHM）system[J]. Advances in aeronautical science and engineering, 2022， 13（3）: 64-73（in Chinese）.

[90] MIN B N， ROSS H， SULEM E， et al. Recent advances in natural language processing via large pre-trained language models:a survey[J]. ACM computing surveys, 2024， 56（2）: 30.

[91] 杨泽文, 郭力, 袁昱超, 等. 大模型驱动的船舶结构健康监测系统关键技术研究综述及展望[J]. 中国舰船研究, 2025， 20（6）: 3-18.

[92] YANG Zewen， GUO Li， YUAN Yuchao， et al. Review and prospects of key technologies for large language model-driven ship structural health monitoring systems[J]. Chinese journal of ship research, 2025， 20（6）: 3-18（in Chinese）.

[93] 雷亚国, 李熹伟, 李响, 等. 面向机械设备通用健康管理的智能运维大模型[J]. 机械工程学报, 2025， 61（6）: 1-13.

[94] LEI Yaguo， LI Xiwei， LI Xiang， et al. Research on large model for general prognostics and health management of machinery[J]. Journal of mechanical engineering, 2025， 61（6）: 1-13（in Chinese）.

[95] KLEKOWICKI M， SZYMAŃSKI G M， WALIGÓRSKI M， et al. Application of large language models in diagnostics and maintenance of aircraft propulsion systems[J]. Advances in science and technology research journal, 2025， 19（2）: 304-320.

基金资助

陕西省自然科学基金资助项目(2023-JC-YB-007)