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飞机结构健康监测技术发展研究

Research on the development of aircraft structural health monitoring technology
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摘要 介绍了结构健康监测技术(Structural Health Monitoring,SHM)的概念以及主动和被动损伤监测方法的原理,分析了飞机结构健康监测技术的国内外研究现状,阐述了比较真空监测(Comparative Vacuum Monitoring,CVM)传感技术、智能涂层传感器技术、光纤传感技术、压电传感器(Piezoelectric Sensors,PZT)技术和无线传感器网络(Wireless Sensor Network,WSN)等目前较为先进的传感技术的原理以及传感器技术在各类装备上的应用情况,介绍了飞机结构健康监测技术在F-35联合攻击机(Joint Strike Fighter,JSF)上的典型应用。指出飞机结构健康监测技术正向智能化方向发展;未来需要重点研究传感器网络的智能诊断技术、复杂环境下的SHM技术、基于结构健康监测的健康管理技术、智能材料/结构健康监测技术,并将深度学习、数字孪生等前沿技术应用于航空领域,以推动我国飞机结构健康监测技术发展。 This article introduces the concept of structural health monitoring(SHM)technology and the principles of active and passive damage monitoring methods,and analyzes the current research status of structural health monitoring technology for aircraft both domestically and internationally.It elaborates on the monitoring principles and applications of advanced sensor technologies such as comparative vacuum monitoring(CVM)sensing technology,intelligent coating sen⁃sor technology,fiber optic sensing technology,piezoelectric sensor(PZT)technology,and wireless sensor network(WSN)in various types of equipment.Typical applications of SHM technology on the F-35 joint strike fighter(JSF)are pre⁃sented.It is pointed out that aircraft SHM technology is developing towards intelligence.In the future,it is necessary to fo⁃cus on the intelligent diagnosis technology of sensor networks,SHM technology in complex environments,health manage⁃ment technology based on SHM technology,health monitoring technology for intelligent materials or structures,and to ap⁃ply frontier technologies such as deep learning and digital twins to the aviation field to promote the development of air⁃craft structural health monitoring technology in China.
作者 刘雪蓉 曹贺 张宝珍 LIU Xuerong;CAO He;ZHANG Baozhen(Aviation Industry Development Research Center of China,Beijing 100029,China)
出处 《计测技术》 2024年第2期13-24,共12页 Metrology & Measurement Technology
基金 航空科学基金项目(2022Z064028002)。
关键词 飞机 结构健康监测 先进传感器 结构预测与健康管理 深度学习 数字孪生 aircraft structural health monitoring advanced sensors prognostics and health management deep learning digital twins
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