Structural Damage Diagnosis of Aerospace CFRP Components: Leveraging Transfer Learning in the Matching Networks Framework

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Zhuojun Xu, Hao Li, Jianbo Yu
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Abstract

This paper introduces a damage diagnosis method based on the reassignment method and matching networks (MNs) to study the structural health monitoring of aerospace composite material components. This aims to facilitate the mapping of signal features to complex failure modes. We introduce a signal processing technique based on the reassignment method, employing a sliding analysis window to re-estimate local instantaneous frequency and group delay. By utilizing the short-time phase spectrum of the signal, we correct the nominal time and frequency coordinates of the spectrum data, aligning them more accurately with the true support region of the analyzed signal. Subsequently, this paper developed a deep matching network (DMN) damage diagnosis model based on MNs. This model utilizes a convolutional neural network (CNN) to extract damage-related features from the signal and introduces the full context embedding (FCE) method to enhance the compatibility of sample embeddings. In this process, the embeddings of each sample in the training set should be mutually independent, while the embeddings of test samples should be regulated by the distribution of training set sample data. Ultimately, the damage category of test samples is determined based on cosine similarity. We validate our model using damage sample data collected from experiments and simulations conducted under varying components and operating conditions. Comparative assessments with five mainstream methods reveal an average accuracy exceeding 96%. This underscores the exceptional recognition accuracy and generalization performance of our proposed method in cross-operating condition fault diagnosis experiments concerning aircraft composite material components.

Abstract Image

航空 CFRP 组件的结构损伤诊断:利用匹配网络框架中的迁移学习
本文介绍了一种基于重新分配法和匹配网络(MN)的损伤诊断方法,用于研究航空航天复合材料部件的结构健康监测。其目的是促进信号特征与复杂失效模式的映射。我们介绍了一种基于重新分配法的信号处理技术,利用滑动分析窗口来重新估计局部瞬时频率和群延迟。通过利用信号的短时相位频谱,我们修正了频谱数据的标称时间和频率坐标,使其更准确地与分析信号的真实支持区域保持一致。随后,本文开发了基于 MN 的深度匹配网络(DMN)损伤诊断模型。该模型利用卷积神经网络(CNN)从信号中提取损伤相关特征,并引入全上下文嵌入(FCE)方法来增强样本嵌入的兼容性。在此过程中,训练集中每个样本的嵌入应相互独立,而测试样本的嵌入则应受训练集中样本数据分布的调节。最终,根据余弦相似度确定测试样本的损坏类别。我们使用在不同部件和运行条件下进行的实验和模拟中收集的损坏样本数据验证了我们的模型。与五种主流方法的比较评估显示,平均准确率超过 96%。这凸显了我们提出的方法在有关飞机复合材料部件的跨运行条件故障诊断实验中卓越的识别准确性和泛化性能。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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