航空结构声发射数据质量评估与疲劳损伤监测方法

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关键词：声发射信号；结构健康监测；卷积自编码器；数据质量评估；疲劳损伤中图分类号：V216.3文献标志码：ADOI： 10.7652/xjtuxb202509001 文章编号：0253-987X（2025）09-0001-10

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

LU Fan，LIXiang，LEIYaguo，LINaipeng，YANGBin （Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System， Xi'an JiaotongUniversity，Xi'an 71oo49，China）

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 intellgent 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 collcted from fatigue tests of aerospace aluminum alloy components. The results demonstrate that the proposed method yields average reconstruction errors of O.O07 and O.O2O 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 22min 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.

Keywords： acoustic emission signal; structural health monitoring; convolutional autoencoder; data quality assessment; fatigue damage

由于航空飞行器长期服役于恶劣工况下，其关键构件在内外动态载荷、腐蚀疲劳等因素的持续性影响下产生疲劳损伤。（剩余13213字）