细长体飞行器模态频率快速预示与结构故障定位技术研究  

Research on Rapid Modal Frequency Prediction and Structural Fault Localization for Slender Body Aircraft

作　　者：沈奕哲 贾骏恺 史晓鸣 李昱霖 华洲 SHEN Yizhe;JIA Junkai;SHI Xiaoming;LI Yuliu;HUA Zhou(Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China)

机构地区：[1]上海机电工程研究所,上海201109

出　　处：《上海航天(中英文)》2025年第2期177-185,共9页Aerospace Shanghai(Chinese&English)

基　　金：国防科工局国防基础科研资助项目(JCKY2023203A003);上海市东方英才计划资助项目(QNZH2024069)。

摘　　要：针对细长体飞行器模态频率快速预示及结构故障定位的需求,提出了一种结合工程先验知识、人工神经网络和参数辨识的高效方法,旨在实现模态频率的快速预测和结构故障的精准定位。基于样本设计和一维质量点刚度梁模型,实现细长体飞行器结构动力学低阶模态频率计算的数据驱动。结合神经网络的快速映射能力,实现了基于模态频率对飞行器全局质量、刚度的高效辨识,在工程实践中准确定位产品头罩质量异常,其质量误差不超过3.13%。To address the needs for the rapid modal frequency prediction and structural fault localization for slender body aircraft,an efficient method combining engineering prior knowledge,artificial neural networks,and parameter identification is proposed.Based on the sample design,the one-dimensional mass-spring-stiffness beam model is used to calculate the low-order modal frequencies of the structural dynamics of a slender body aircraft.By leveraging the rapid mapping capability of neural networks,the global mass and stiffness of the aircraft are efficiently identified based on the modal frequencies achieved.In engineering practice,the nose cone mass anomalies are accurately located,and the quality error does not exceed 3.13%.

关 键 词：结构动力学 细长体飞行器 神经网络 参数辨识 故障诊断 

分 类 号：V214.3[航空宇航科学与技术—航空宇航推进理论与工程]