Research and Application Progress on Damage Detection for High-Temperature Structures of the Aero-Engines

ZHOU Yunlai; WEN Shenglin; DONG Xinyu; LIU Wei; LU Liang; ZHANG Xiaoming; YANG Qiang

doi:10.7652/xjtuxb202606007

您当前的位置：

首页 >

文章列表页 >

Research and Application Progress on Damage Detection for High-Temperature Structures of the Aero-Engines

Special Topic Advanced Technology of Nondestructive Testing | 更新时间：2026-06-12

- Research and Application Progress on Damage Detection for High-Temperature Structures of the Aero-Engines
- Journal of Xi'an Jiaotong University Vol. 60, Issue 6, Pages: 86-96(2026)
- 作者机构：
  
  西安交通大学复杂服役环境重大装备结构强度与寿命全国重点实验室，710049，西安
  空军工程大学航空工程学院，710038，西安
  暨南大学公共管理学院/应急管理学院，510632，广州
  陕西省特种设备检验检测研究院，710048，西安
  中国飞机强度研究所强度与结构完整性全国重点实验室，710065，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202606007
  CLC： V263.6
- Received：23 September 2025，
  
  Published：10 June 2026
- 稿件说明：
移动端阅览
ZHOU Yunlai, WEN Shenglin, DONG Xinyu, et al. Research and Application Progress on Damage Detection for High-Temperature Structures of the Aero-Engines[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 86-96.
DOI：

ZHOU Yunlai, WEN Shenglin, DONG Xinyu, et al. Research and Application Progress on Damage Detection for High-Temperature Structures of the Aero-Engines[J]. Journal of Xi'an Jiaotong University, 2026, 60(6): 86-96. DOI： 10.7652/xjtuxb202606007.

摘要

航空发动机作为航空装备的“动力心脏”，其高温结构（如涡轮叶片、燃烧室）在高温、高压、强振动等极端环境下的损伤失效直接威胁着航空装备安全，是制约航空装备向隐身化、超音速化、长寿命化发展的核心瓶颈之一。首先，从航空发动机高温结构损伤智能感知、检测评估、先进测试等层面出发，综述了航空发动机高温结构损伤检测领域的研究与应用进展。接着，针对航空发动机高温结构损伤检测，分别总结了基于航空发动机定期检测和基于飞参总线数据判读两方面的研究进展，介绍了新型检测方法在航空发动机高温结构中的创新应用实践，并总结了现有的采用数据驱动方式实现损伤早期识别和趋势预测的应用研究。然后，在模型驱动方面，阐述了集成物理机理模型和采用智能算法提升预测性能的可靠性，以及机器学习与深度学习算法同步处理多源数据，从而实现航空发动机高温结构损伤快速检测的研究现状。最后，梳理了相关检测技术和判读技术发展历程，并结合航空装备应用需求展望了未来发展趋势。

Abstract

The aero-engine is the “power heart”of aero-equipment.Its high-temperature structures（e.g.，turbine blades and combustors）may suffer damage or failure when exposed to extreme conditions such as high temperature，high pressure，and intense vibration，which will directly threaten the operational safety of aero-equipment.These structures constitute one of the key bottlenecks that constrain the evolution of aero-equipment towards stealth，hypersonic capability，and extended service life.First，from the perspectives of intelligent sensing，detection and evaluation，and advanced testing，the research and application progress on damage detection for high-temperature structures of aero-engines is reviewed.Next，research advances on the damage detection of high-temperature structures of aero-engines are summarized in terms of periodic aeroengine inspection and interpretation of flight-parameter bus data，and innovative applications of novel detection methods to high-temperature structures of aero-engines are introduced；furthermore，existing application studies that employ data-driven approaches for early damage identification and trend prediction are also summarized.Subsequently，in the model-driven domain，the integration of physics-based mechanism models and the use of intelligent algorithms to improve prediction reliability are described，and the current state of research in which machine learning and deep learning algorithms are used to synchronously process multi-source data for rapid detection of damage to high-temperature structures of aero-engines is presented.Finally，the development history of related detection and interpretation technologies is outlined，and future development trends are forecasted in light of aero-equipment application requirements.

关键词

Keywords

references

王者，王志平，丁坤英，等.某型航空发动机涡轮导向叶片热障涂层可靠性分析[J].航空学报，2024，45（22）：281-295.

WANG Zhe，WANG Zhiping，DING Kunying，et al. Reliability analysis of thermal barrier coatings on turbine guide vanes of a certain type of aero-engine[J]. Acta Aeronautica et Astronautica Sinica，2024，45（22）：281-295.

宋迎东，凌晨，张磊成，等.航空发动机和燃气轮机热端部件热腐蚀-疲劳研究进展[J].南京航空航天大学学报，2022，54（5）：771-788.

SONG Yingdong，LING Chen，ZHANG Leicheng，et al.Research progress on hot corrosion-fatigue of aeroengine and gas turbine hot-section components [J]. Journal of Nanjing University of Aeronautics & Astronautics，2022，54（5）：771-788.

DE GIORGI M G，MENGA N，MOTHAKANI A，et al. A data-driven approach for health status assessment and remaining useful life prediction of aero-engine [J]. Journal of Physics：Conference Series，2023，2526（1）：012071.

黄金泉，王启航，鲁峰.航空发动机气路故障诊断研究现状与展望[J].南京航空航天大学学报，2020，52（4）：507-522.

HUANG Jinquan，WANG Qihang，LU Feng. Research status and prospect of gas path fault diagnosis for aeroengine [J].Journal of Nanjing University of Aeronautics & Astronautics，2020，52（4）：507-522.

何宇豪，曹学国，刘信良，等.基于SW-YOLO模型的航空发动机叶片损伤实时检测[J].推进技术，2024，45（2）：192-198.

HE Yuhao，CAO Xueguo，LIU Xinliang，et al.Real time detection of aircraft engine blade damage based on SW-YOLO model[J].Journal of Propulsion Technology，2024，45（2）：192-198.

李华，陈果，陈新波，等.航空发动机内部裂纹自动测量方法研究[J].计算机工程与应用，2016，52（11）：233-237.

LI Hua，CHEN Guo，CHEN Xinbo，et al.Study on automatic measurement method for aero-engine inner damage crack[J].Computer Engineering and Applications，2016，52（11）：233-237.

赵广社，吴思思，荣海军.多源统计数据驱动的航空发动机剩余寿命预测方法[J].西安交通大学学报，2017，51（11）：150-155.

ZHAO Guangshe，WU Sisi，RONG Haijun.A multisource statistics data-driven method for remaining useful life prediction of aircraft engine[J].Journal of Xi’an Jiaotong University，2017，51（11）：150-155.

李伟业，冯建欣，文思果，等.时序卷积网络在转子热固耦合应力预测及寿命评估中的应用[J].西安交通大学学报，2025，59（10）：87-95.

LI Weiye，FENG Jianxin，WEN Siguo，et al.Application of temporal convolutional network in rotor thermo-mechanical coupling stress prediction and life assessment[J].Journal of Xi’an Jiaotong University，2025，59（10）：87-95.

杨晓光，谭龙，郝文琦，等.数据驱动的高温结构强度与寿命评估：进展与挑战[J].推进技术，2023，44（5）：1-18.

YANG Xiaoguang，TAN Long，HAO Wenqi，et al. Data-driven structural strength and life assessment of high temperature structure：progresses and challenges[J]. Journal of Propulsion Technology，2023，44（5）：1-18.

邓沛洲，武晓东.航空发动机孔探技术探索与研究[J].机械工业标准化与质量，2023（8）：33-36.

DENG Peizhou，WU Xiaodong.Exploration and research on hole-drilling technology for aeroengine[J]. Machinery Industry Standardization & Quality，2023（8）：33-36.

何宇豪.基于深度学习和孔探技术的航空发动机叶片损伤检测[D].南京：南京航空航天大学，2023.

李续博，王文庆，王凯，等.人工智能技术在航空发动机孔探检测中的应用进展[J].航空工程进展，2023，14（2）：12-23.

LI Xubo，WANG Wenqing，WANG Kai，et al.Application advances of artificial intelligence technology in aero-engine borescope inspection [J]. Advances in Aeronautical Science and Engineering，2023，14（2）：12-23.

SHENG Hanlin，LIU Tong，ZHAO Yan，et al.New model-based method for aero-engine turbine blade tip clearance measurement[J].Chinese Journal of Aeronautics，2023，36（8）：128-147.

蔡舒妤，何冲.基于FDG-YOLO轻量化模型的航空发动机损伤检测方法[J/OL].北京航空航天大学学报.（2024-05-24）[2025-04-10].https：//doi.org/10.13700/j.bh.1001-5965.2024.0024.

CAI Shuyu，HE Chong.A damage detection method for aero-engine based onFDG-YOLOlightweight model [J/OL].Journal of Beijing University of Aeronautics and Astronautics.（2024-05-24）[2025-04-10].https：//doi.org/10.13700/j.bh.1001-5965.2024.0024.

宋潮，郑宾，郭华玲，等.基于小波包能量变化率的激光超声处理[J].中国测试，2017，43（11）：12-16.

SONG Chao，ZHENG Bin，GUO Hualing，et al.Laser ultrasonic treatment based on wavelet packet energy change rate[J].China Measurement & Test，2017，43（11）：12-16.

DU Xiao，CHEN Jiajie，ZHANG Haibo，et al.Fault detection of aero-engine sensor based on inceptionCNN[J].Aerospace，2022，9（5）：236.

MOHAMMED ELTOUM M A，IQBAL E，ZWEIRI Y，et al.BladeSynth：a high-quality rendering-based synthetic dataset for aero engine blade defectinspection [J].Scientific Data，2025，12（1）：1268.

蔡舒妤，闫子砚.基于改进YOLOv4的航空发动机小目标损伤检测研究[J].航空动力学报，2023，38（2）：445-452.

CAI Shuyu，YAN Ziyan.Research on small target damage detection of aero-engine based on improved YOLOv4[J].Journalof Aerospace Power，2023，38（2）：445-452.

刘超.涡流检测快速缺陷反演方法研究[D].厦门：厦门大学，2016.

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）：248-260.

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）：248-260.

WANG Cunsong，LU Ningyun，CHENG Yuehua，et al.A data-driven aero-engine degradation prognostic strategy[J].IEEE Transactions on Cybernetics，2021，51（3）：1531-1541.

SHANG Hongbing，SUN Chuang，LIU Jinxin，et al. Deep learning-based borescope image processing for aero-engine blade in-situ damage detection[J].Aerospace Science and Technology，2022，123：107473.

周胜明，王小飞，高峰，等.基于在线SVDD的航空发动机异常检测方法[J].计测技术，2015（5）：20-22.

ZHOU Shengming，WANG Xiaofei，GAO Feng，et al.Aeroengine novelty detection method based on onlineSVDD[J].Metrology & Measurement Technology，2015（5）：20-22.

冯迪，孙君如，魏鹏，等.基于光纤传感的航空发动机涡轮转子温度-应变复合信号测量方法研究[J/OL].航空动力学报.（2025-06-26）[2025-09-07].https：//doi.org/10.13224/j.cnki.jasp.20250068.

FENG Di，SUN Junru，WEI Peng，et al.Research on the measurement method of temperature-strain composite signal of turbine rotor in aeronautical engine based on fiber optic sensing[J/OL].Journal of Aerospace Power.（2025-06-26）[2025-09-07].https：//doi.org/10.13224/j.cnki.jasp.20250068.

邹仕军，王雪婷，杜新悦.光纤光栅（FBG）传感器在飞行试验应变测量中的应用[J].自动化应用，2023，64（9）：201-203.

ZOU Shijun，WANG Xueting，DU Xinyue.Application of fiber Bragg grating（FBG）sensor in flight test strain measurement[J].Automation Application，2023，64（9）：201-203.

李天梁，温宇航，张奥，等.一种基于“8”型弹簧结构的高温大应变光纤传感器[J].航空制造技术，2025，68（17）：34-41.

LI Tianliang，WEN Yuhang，ZHANG Ao，et al.A high-temperature large-strain optical fiber sensor based on “8”shaped spring structure[J].Aeronautical Manufacturing Technology，2025，68（17）：34-41.

MOHAMMADI S，RAHMANIAN V，SATTARPANAH KARGANROUDI S，et al. Smart defect detection in aero-engines：evaluating transfer learning withVGG19 anddata-efficientimagetransformer models[J].Machines，2025，13（1）：49.

张子瑞，高尚.无线无源多栅格结构式温压传感器设计与制备[J].微纳电子技术，2024，61（3）：112-119.

ZHANG Zirui，GAO Shang.Design and fabrication of wireless passive multi-grid structural temperature and pressure sensors [J].Micronanoelectronic Technology，2024，61（3）：112-119.

ZHOU Zhixin，WANG Hui，CHEN Minkan，et al. Design and characterization of SAW-based wireless and passive temperature sensing system [J].Journal of Physics：Conference Series，2024，2822（1）：012098.

谭秋林，刘佳佳，王宁，等.无线无源力/热传感器与测试技术研究现状[J].振动、测试与诊断，2024，44（3）：419-430.

TAN Qiulin，LIU Jiajia，WANG Ning，et al.Current research status of wireless passive force/thermal sensors and testing technology[J].Journal of Vibration，Measurement & Diagnosis，2024，44（3）：419-430.

MILOJEVI https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=110292025&type= https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=110292025&type=small https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=110292025&type=middle I，TARI https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=110292056&type= https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=110292056&type=small https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=110292056&type=middle M，KLIMENTA D，et al. Finite element analysis-based assessment of damage parameters for ultra-low-cycle fatigue behavior of structural steels[J ] .Symmetry，2025，174（4）：615.

洪海铭，詹志新，王佳莹.基于损伤力学的增材制造金属材料疲劳寿命预测[J].北京航空航天大学学报，2022，48（6）：950-956.

HONG Haiming，ZHAN Zhixin，WANG Jiaying. Damage mechanics-based fatigue life prediction for additive manufacturing metal materials [J].Journal of Beijing University of Aeronautics and Astronautics，2022，48（6）：950-956.

胡殿印，鄢林，李鑫，等.涡轮榫接疲劳寿命评估及验证：研究现状及展望[J].南京航空航天大学学报，2023，55（4）：573-588.

HU Dianyin，YAN Lin，LI Xin，et al.Fatigue life assessment and verification of turbine attachment：review and prospects [J].Journal of Nanjing University of Aeronautics & Astronautics，2023，55（4）：573-588.

ZHOU Mingyang，MIAO Keqiang，SUN Jiaxian，et al.Data-driven modeling of aero-engine performance degradation models [J]. IEEE Access，2024，12：150020-150031.

BAKTHEER A，ALDAKHEEL F.Physics-based machine learning for fatigue lifetime prediction under non-uniform loading scenarios[J].Computer Methods in Applied Mechanics and Engineering，2025，444：118116.

YANG Zhengmao，LI Wenhao，CHEN Yang，et al. Life assessment of thermomechanical fatigue in a woven SiC/SiC ceramic matrix composite with an environmental barrier coating at elevated temperature[J].International Journal of Fatigue，2023，172：107584.

SZUSTA J，SEWERYNA.Fatigue damage accumulation modeling of metals alloys under high amplitude loading at elevated temperatures [J].Metals，2018，8（12）：1030.

李双宝，李芳.高温环境下发动机叶片-硬涂层阻尼结构动力学建模与分析[J].中国民航大学学报，2024，42（1）：30-39.

LI Shuangbao，LI Fang.Dynamic modeling and analysis of engine blade-hard coating damping structure under high temperature environment[J].Journal of Civil Aviation University of China，2024，42（1）：30-39.

孙旭呈，荆建平，温猛，等.力热载荷作用下涡轮结构模态与振动响应特性研究[J].噪声与振动控制，2025，45（6）：19-25.

SUN Xucheng，JING Jianping，WEN Meng，et al. Modal and vibration response characteristics of power turbine blade-disk structures under mechanical and thermal loads[J].Noise and Vibration Control，2025，45（6）：19-25.

李浦，袁奇.拉杆转子接触界面参数识别和动力学研究进展[J].西安交通大学学报，2023，57（10）：39-52.

LI Pu，YUAN Qi.Research progress oninterface parameter identification and dynamics of tie-bolt rotors [J]. Journal of Xi’an Jiaotong University，2023，57（10）：39-52.

WANG Yuting，LIU Feng，XI Feng，et al.Data driven comprehensive performance evaluation of aeroengines：a network dynamic approach [J].Engineer ing，2025，46：292-305.

TARAFDER P，DAN S，GHOSH S.Machine learning-enabled parametrically upscaled coupled constitutive damage mechanics（PUCCDM）model for multiscale-multiphysics analysis of piezoelectric composites[J].Computer Methods in Applied Mechanics and Engineering，2025，444：118136.

丁珈，周炜，张永，等.数字孪生驱动的装备全寿命周期智能运维技术进展与展望[J].系统工程理论与实践，2025，45（6）：1828-1845.

DING Jia，ZHOU Wei，ZHANG Yong，et al.Digital twin-driven full lifecycle intelligent equipment maintenance：progress and prospects[J].Systems Engineering-Theory & Practice，2025，45（6）：1828-1845.

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.

甘磊，吴昊，仲政.数据驱动的金属疲劳寿命模型研究进展[J].力学进展，2025，55（1）：30-79.

GAN Lei，WU Hao，ZHONG Zheng.Advances in data-driven models for fatigue life prediction of metallic materials[J].Advances in Mechanics，2025，55（1）：30-79.

王倩岚，刘文波，滕子煜，等.基于改进YOLOv4的航空发动机叶片损伤检测[J].机械制造与自动化，2025，54（2）：223-226.

WANG Qianlan，LIU Wenbo，TENG Ziyu，et al. Aeroengine blade damage detection based on improved YOLOv4 [J].MachineBuilding & Automation，2025，54（2）：223-226.

何振鹏，刘鸿宇，张保深，等.基于改进YOLOv8模型的航空发动机叶片损伤检测算法研究[J].推进技术，2026，47（1）：293-302.

HE Zhenpeng，LIU Hongyu，ZHANG Baoshen，et al. Aero engine blade damage detection based on improved YOLOv8[J].Journal of Propulsion Technology，2026，47（1）：293-302.

于婧仪.基于深度学习的航空发动机叶片损伤检测技术研究[D].天津：中国民航大学，2023.

黄睿，段博坤，陈望，等.检测器和分类器级联的飞机发动机损伤检测[J].中国图象图形学报，2022，27（11）：3232-3242.

HUANG Rui，DUAN Bokun，CHEN Wang，et al. Detector and classifier cascaded aero-engine detection method[J].Journal of Image and Graphics，2022，27（11）：3232-3242.

王栋欢，于艾洋，肖洪.基于无监督学习的航空发动机铸造涡轮叶片缺陷检测方法[J].航空动力学报，2025，40（6）：247-258.

WANG Donghuan，YU Aiyang，XIAO Hong.Defect detection method for casting turbine blades in aeroengines based on unsupervised learning[J].Journal of Aerospace Power，2025，40（6）：247-258.

赵崇林，朱江，胡永进，等.融合注意力和多尺度特征的航空发动机缺陷检测[J].北京航空航天大学学报，2025，51（3）：892-903.

ZHAO Chonglin，ZHU Jiang，HU Yongjin，et al. Aero-engine defect detection by fusing attention and multi-scale features[J].Journal of Beijing University of Aeronautics and Astronautics，2025，51（3）：892-903.

FU Chun，JIANG Shaofei.A hybrid data-fusion system by integrating CFD and PNN for structural damage identification[J].Applied Sciences，2021，11（17）：8272.

何超，陈果，王雨薇.基于深度学习的航空发动机内部损伤实时检测方法[J].航空动力学报，2023，38（8）：1857-1864.

HE Chao，CHEN Guo，WANG Yuwei.Real-time detection method of aero-engine internal damage based on deep learning[J].Journal of Aerospace Power，2023，38（8）：1857-1864.

严如强，商佐港，王志颖，等.可解释人工智能在工业智能诊断中的挑战和机遇：先验赋能[J].机械工程学报，2024，60（12）：1-20.

YAN Ruqiang，SHANG Zuogang，WANG Zhiying，et al.Challenges and opportunities of XAI in industrial intelligent diagnosis：priori-empowered[J].Journal of Mechanical Engineering，2024，60（12）：1-20.

ABDULKADIROV R，LYAKHOV P，BUTUSOV D，et al.Physics-aware machine learning approach for high-precision quadcopter dynamics modeling [J]. Drones，2025，9（3）：187.

DE SILVA B M，CALLAHAM J，JONKER J，et al. Hybrid learning approach to sensor fault detection with flight test data[EB/OL].[2025-06-20].https：//arc. aiaa.org/doi/10.2514/1.J059943.

陆晓华，左洪福，孙见忠.基于检查数据和物理退化模型的涡轮叶片检修策略优化[J].南京航空航天大学学报，2018，50（5）：679-686.

LU Xiaohua，ZUO Hongfu，SUN Jianzhong.Optimizing inspection policy for turbine blades based on datadriven and physical deterioration model[J].Journal of Nanjing University of Aeronautics & Astronautics，2018，50（5）：679-686.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on Film Cooling Characteristics of Trailing Edge Cutback with Dimples

Quantitative Nondestructive Evaluation of Mechanical Damages Based on the Phenomena of Natural Magnetization

Aero-Engine Fan Blade Vibration Characteristic Analysis

Comprehensive Optimization for the Structure of Three-Phase Spacer in 252 kV Compact Gas Insulated Metal Enclosed Transmission Line

I/O Performance Modeling and Prediction Method for Large-Scale Parallel Applications Using Machine Learning

Related Author

Yang Qiang

Lu Liang

Liu Wei

Dong Xinyu

Wen Shenglin

ZHOU Yunlai

YAN Xin

WANG Xi

Related Institution

State Key Laboratory of Strength and Structural Integrity, Aircraft Strength Research Institute of China

School of Public Administration / Emergency Management, Jinan University

School of Aeronautics Engineering, Air Force Engineering University

State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University

(School of Energy and Power Engineering, Xi'an Jiaotong University,,)

⁰