航空结构声发射数据质量评估与疲劳损伤监测方法

鲁凡; 李响; 雷亚国; 李乃鹏; 杨彬

doi:10.7652/xjtuxb202509001

您当前的位置：

首页 >

文章列表页 >

航空结构声发射数据质量评估与疲劳损伤监测方法

专题　第二十七届中国科协年会学术论文 | 更新时间：2025-09-11

- 航空结构声发射数据质量评估与疲劳损伤监测方法
  增强出版
- Method of Acoustic Emission Data Quality Assessment and Fatigue Damage Monitoring for Aeronautical Structures
- 西安交通大学学报 2025年59卷第9期页码：1-10
- 作者机构：
  
  西安交通大学现代设计及转子轴承系统教育部重点实验室， 710049，西安
- 作者简介：
  
  鲁凡（2002—），男，硕士生
  李响，男，教授，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(52005086);航空科学基金资助项目(2024Z071070001);新基石科学基金会所设立的科学探索奖资助项目
- DOI：10.7652/xjtuxb202509001
  中图分类号： V216.3
- 收稿：2025-02-16，
  
  网络首发：2025-05-12，
  
  纸质出版：2025-09-10
- 稿件说明：
移动端阅览
鲁凡, 李响, 雷亚国, 等. 航空结构声发射数据质量评估与疲劳损伤监测方法[J]. 西安交通大学学报, 2025,59(9):1-10.

LU Fan, LI Xiang, LEI Yaguo, et al. Method of Acoustic Emission Data Quality Assessment and Fatigue Damage Monitoring for Aeronautical Structures[J]. Journal of Xi’an Jiaotong University, 2025, 59(9): 1-10.
鲁凡, 李响, 雷亚国, 等. 航空结构声发射数据质量评估与疲劳损伤监测方法[J]. 西安交通大学学报, 2025,59(9):1-10. DOI： 10.7652/xjtuxb202509001.

LU Fan, LI Xiang, LEI Yaguo, et al. Method of Acoustic Emission Data Quality Assessment and Fatigue Damage Monitoring for Aeronautical Structures[J]. Journal of Xi’an Jiaotong University, 2025, 59(9): 1-10. DOI： 10.7652/xjtuxb202509001.

摘要

针对声发射技术在监测航空结构微裂纹萌生拓展时面临的结构微损伤特征难以提取、噪声干扰下损伤定位精度不高等问题，提出了面向航空结构健康监测的声发射数据质量评估与疲劳损伤监测方法。基于深度卷积自编码器，建立了声发射信号的数据质量智能评估模型，通过提取原始声发射数据的高层特征，实现了裂纹损伤与噪声信号的自适应识别，完成了声发射信号的自动降噪。采用航空铝合金结构件疲劳试验中采集的声发射监测数据对所提方法进行实验验证，结果表明：所提方法计算得到早期健康阶段数据和中后期损伤阶段数据的平均重构误差分别为0.007和0.020，准确地实现了噪声信号与损伤信号的有效甄别。损伤起始时间比试验中发现宏观裂纹的时间早22 min，能够在裂纹萌生拓展的早期阶段有效预警。与原始定位图相比，在剔除了噪声信号后进行损伤拓展定位，能够清晰地呈现出裂纹的长期发展趋势。实验结果证明了所提方法具有在工程场景下应用的潜力。

Abstract

To address the challenges in monitoring micro-crack initiation and propagation in aeronautical structures using acoustic emission (AE) technology

including difficulties in extracting micro-damage features and low damage localization accuracy under noise interference

this study proposes a method that enhances aeronautical structural health monitoring through AE data quality assessment and fatigue damage monitoring for aeronautical structures. The method establishes an intelligent quality assessment model for AE signals based on a deep convolutional autoencoder

which extracts high-level features from raw AE data to achieve adaptive discrimination between crack-induced signals and noise

enabling automatic denoising of AE signals. Experimental validation is conducted using AE monitoring data collected from fatigue tests of aerospace aluminum alloy components. The results demonstrate that the proposed method yields average reconstruction errors of 0.007 and 0.020 for data from the early healthy stage and mid-to-late damage stage

respectively

achieving accurate differentiation between noise and damage signals. The detected damage initiation time is 22 min earlier than the macroscopic crack observation time in the experiment

proving effective for early warning during crack initiation and propagation. Compared to original localization maps

the damage progression localization after noise removal clearly reveals the long-term crack development trend. The experimental results demonstrate the potential applicability of the proposed method in engineering scenarios.

关键词

Keywords

references

房芳 , 郑辉 , 汪玉 , 等 . 机械结构健康监测综述 [J ] . 机械工程学报 , 2021 , 57 ( 16 ): 269 - 292 .

FANG Fang , ZHENG Hui , WANG Yu , et al . Mechanical structural health monitoring: a review [J ] . Journal of Mechanical Engineering , 2021 , 57 ( 16 ): 269 - 292 .

GHOLIZADEH S , LEMAN Z , BAHARUDIN B T H T . A review of the application of acoustic emission technique in engineering [J ] . Structural Engineering and Mechanics , 2015 , 54 ( 6 ): 1075 - 1095 .

VERSTRYNGE E , LACIDOGNA G , ACCORNERO F , et al . A review on acoustic emission monitoring for damage detection in masonry structures [J ] . Construction and Building Materials , 2021 , 268 : 121089 .

MCCRORY J P , VINOGRADOV A , PEARSON M R , et al . Acoustic emission monitoring of metals [M ] // GROSSE C U , OHTSU M , AGGELIS D G , et al . Acoustic Emission Testing: Basics for Research-Applications in Engineering . Cham, Belin : Springer International Publishing , 2022 : 529 - 565 .

杨宇 , 王彬文 , 祁小凤 . 面向全尺寸民机结构疲劳试验的声发射监测技术 [J ] . 航空学报 , 2022 , 43 ( 6 ): 241 - 253 .

YANG Yu , WANG Binwen , QI Xiaofeng . Acoustic emission monitoring technology for fatigue test of full-scale civil aircraft structure [J ] . Acta Aeronautica et Astronautica Sinica , 2022 , 43 ( 6 ): 241 - 253 .

王彬文 , 陈先民 , 苏运来 , 等 . 中国航空工业疲劳与结构完整性研究进展与展望 [J ] . 航空学报 , 2021 , 42 ( 5 ): 1 - 39 .

WANG Binwen , CHEN Xianmin , SU Yunlai , et al . Research progress and prospect of fatigue and structural integrity for aeronautical industry in China [J ] . Acta Aeronautica et Astronautica Sinica , 2021 , 42 ( 5 ): 1 - 39 .

白生宝 , 肖迎春 , 杨宇 , 等 . 飞机结构强度试验中的声发射应用进展 [J ] . 环境技术 , 2021 , 39 ( 5 ): 121 - 124 .

BAI Shengbao , XIAO Yingchun , YANG Yu , et al . Applicate progress of acoustic emission technology in the aircraft structure test [J ] . Environmental Technology , 2021 , 39 ( 5 ): 121 - 124 .

DONG Longjun , ZHANG Yihan , BI Shuijin , et al . Uncertainty investigation for the classification of rock micro-fracture types using acoustic emission parameters [J ] . International Journal of Rock Mechanics and Mining Sciences , 2023 , 162 : 105292 .

JU Shiyuan , LI Dongsheng , JIA Jinqing . Machine-learning-based methods for crack classification using acoustic emission technique [J ] . Mechanical Systems and Signal Processing , 2022 , 178 : 109253 .

ZHANG Zhenghu , DENG Jianhui . A new method for determining the crack classification criterion in acoustic emission parameter analysis [J ] . International Journal of Rock Mechanics and Mining Sciences , 2020 , 130 : 104323 .

YANG Zhensheng , YAN Wei , JIN Li , et al . A novel feature representation method based on original waveforms for acoustic emission signals [J ] . Mechanical Systems and Signal Processing , 2020 , 135 : 106365 .

雷亚国 , 杨彬 , 李乃鹏 , 等 . 跨设备的机械故障靶向迁移诊断方法 [J ] . 机械工程学报 , 2022 , 58 ( 12 ): 1 - 9 .

LEI Yaguo , YANG Bin , LI Naipeng , et al . Targeted transfer diagnosis method across different machines [J ] . Journal of Mechanical Engineering , 2022 , 58 ( 12 ): 1 - 9 .

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 .

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 .

李响 , 徐宜销 , 雷亚国 , 等 . 面向旋转机械装备的智能故障诊断通用基础模型研究 [J ] . 西安交通大学学报 , 2025 , 59 ( 7 ): 1 - 12 .

LI Xiang , XU Yixiao , LEI Yaguo , et al . Research on general foundation model of intelligent fault diagnosis for rotating machines [J ] . Journal of Xi'an Jiaotong University , 2025 , 59 ( 7 ): 1 - 12 .

李响 , 付春霖 , 雷亚国 , 等 . 保证数据隐私的装备协同智能故障诊断联邦迁移学习方法 [J ] . 机械工程学报 , 2023 , 59 ( 6 ): 1 - 9 .

LI Xiang , FU Chunlin , LEI Yaguo , et al . Federated transfer learning method for privacy-preserving collaborative intelligent machinery fault diagnostics [J ] . Journal of Mechanical Engineering , 2023 , 59 ( 6 ): 1 - 9 .

贾峰 , 李世豪 , 沈建军 , 等 . 采用深度迁移学习与自适应加权的滚动轴承故障诊断 [J ] . 西安交通大学学报 , 2022 , 56 ( 8 ): 1 - 10 .

JIA Feng , LI Shihao , SHEN Jianjun , et al . Fault diagnosis of rolling bearings using deep transfer learning and adaptive weighting [J ] . Journal of Xi'an Jiaotong University , 2022 , 56 ( 8 ): 1 - 10 .

SIKDAR S , LIU Dianzi , KUNDU A . Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel [J ] . Composites: Part B Engineering , 2022 , 228 : 109450 .

BARILE C , CASAVOLA C , PAPPALETTERA G , et al . Damage monitoring of carbon fibre reinforced polymer composites using acoustic emission technique and deep learning [J ] . Composite Structures , 2022 , 292 : 115629 .

KÖNIG F , MARHEINEKE J , JACOBS G , et al . Data-driven wear monitoring for sliding bearings using acoustic emission signals and long short-term memory neural networks [J ] . Wear , 2021 , 476 : 203616 .

DU Jinbo , ZENG Jie , CHEN Chao , et al . Acoustic emission monitoring for damage diagnosis in composite laminates based on deep learning with attention mechanism [J ] . Mechanical Systems and Signal Processing , 2025 , 222 : 111770 .

LI Dan , NIE Jiahao , WANG Hao , et al . Loading condition monitoring of high-strength bolt connections based on physics-guided deep learning of acoustic emission data [J ] . Mechanical Systems and Signal Processing , 2024 , 206 : 110908 .

李睿 , 张纯 , 万乐 , 等 . 基于无监督深度学习的声发射信号聚类分析 [J ] . 无损检测 , 2021 , 43 ( 2 ): 5 - 10 .

LI Rui , ZHANG Chun , WAN Le , et al . Clustering analysis of acoustic emission signals based on unsupervised deep learning [J ] . Nondestructive Testing , 2021 , 43 ( 2 ): 5 - 10 .

张西宁 , 向宙 , 唐春华 . 一种深度卷积自编码网络及其在滚动轴承故障诊断中的应用 [J ] . 西安交通大学学报 , 2018 , 52 ( 7 ): 1 - 8 .

ZHANG Xining , XIANG Zhou , TANG Chunhua . A deep convolutional auto-encoding neural network and its application in bearing fault diagnosis [J ] . Journal of Xi'an Jiaotong University , 2018 , 52 ( 7 ): 1 - 8 .

BEHNIA A , CHAI H K , GHASEMIGOL M , et al . Advanced damage detection technique by integration of unsupervised clustering into acoustic emission [J ] . Engineering Fracture Mechanics , 2019 , 210 : 212 - 227 .

贺谦 , 韩凯 , 冯建民 , 等 . 任意构型传声器阵列声源定位方法 [J ] . 西安交通大学学报 , 2020 , 54 ( 4 ): 186 - 192 .

HE Qian , HAN Kai , FENG Jianmin , et al . A sound source localization method for microphone array with arbitrary configuration [J ] . Journal of Xi'an Jiaotong University , 2020 , 54 ( 4 ): 186 - 192 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

特种产品运输振动加速模拟试验条件的研究

面向缩聚模型的结构损伤识别研究

夹芯复合材料T型接头弯曲疲劳损伤机制及剩余强度试验研究

复杂载荷下轴流压气机叶片疲劳损伤数值研究

数字孪生驱动的小样本旋转机械剩余寿命预测