| 摘要: |
| 螺栓连接易于组装和承载能力强，被广泛用于飞行器部组件连接。由于设计和安装不当、服役载荷和环境影响，螺栓连接容易失去预紧力。螺栓连接松动会降低结构的可靠性，甚至导致结构失效。准确监测螺栓预紧力对于确保飞行器结构的可靠性和安全性具有重要意义。超声导波和机电阻抗技术是常用的结构健康监测方法。近年来机器学习技术，特别是深度学习技术迅速发展，受到了广泛关注。针对基于压电传感器的螺栓预紧力智能监测方法进行了综述，包括传统机器学习技术和深度学习技术的应用，梳理预紧力智能监测的发展现状和重要进展，为推动连接结构健康监测的技术发展和工程应用提供参考。 |
| 关键词: 螺栓松动 超声导波 机电阻抗 机器学习 深度学习 |
| DOI： |
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| 基金项目:国家自然科学基金（12072268）；内蒙古自治区自然科学基金（2023ZD12） |
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| Intelligent Monitoring Technology of Bolt Loosening Based on Piezoelectric Sensor |
| DU Fei,TIAN Zhenxiong,XU Chao,PANG Keji,YANG Xukun |
| (School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China) |
| Abstract: |
| Bolted joints are widely used for component connections due to their ease of assembly and high load-bearing capacity. However, due to improper installation and external loads, bolted connections are prone to loss of preload. Bolt loosening would reduce the reliability of the structure and even lead to structural failure. Therefore, accurate monitoring of bolt preload is crucial to ensure the reliability and safety of the structure. Ultrasonic guided wave and electromechanical impedance techniques are commonly employed for structural health monitoring. In recent years, the rapid development of machine learning techniques, especially deep learning techniques, has attracted widespread attention. Hence, this paper presents a review of intelligent monitoring methods for bolt preload force based on piezoelectric sensors, including the application of traditional machine learning techniques and deep learning techniques. The purpose is to sort out the current development status and important breakthroughs in the intelligent monitoring of bolt preload and provide a reference for promoting the technical development and engineering application in the field of structural health monitoring. |
| Key words: Bolt loosening monitoring Guided waves Electro-mechanical impedance Machine learning Deep learning |