Method of Acoustic Emission Data Quality Assessment and Fatigue Damage Monitoring for Aeronautical Structures

LU Fan; LI Xiang; LEI Yaguo; LI Naipeng; YANG Bin

doi:10.7652/xjtuxb202509001

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Method of Acoustic Emission Data Quality Assessment and Fatigue Damage Monitoring for Aeronautical Structures

Special Topic　Academic Papers of the 27th Annual Meeting of the China Association for Science and Technology | 更新时间：2025-09-11

- Method of Acoustic Emission Data Quality Assessment and Fatigue Damage Monitoring for Aeronautical Structures
  增强出版
- Journal of Xi’an Jiaotong University Vol. 59, Issue 9, Pages: 1-10(2025)
- 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：
- 基金信息：
- DOI：10.7652/xjtuxb202509001
  CLC： V216.3
- Received：16 February 2025，
  
  Online First：12 May 2025，
  
  Published：10 September 2025
- 稿件说明：
移动端阅览
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：

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.

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Keywords

references

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