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使用SH型引导波检测多重螺栓松动:将物理机制与监控数据集成
Xiaodong Sui1, Ru Zhang2, Yaozhi Luo3
1College of Civil Engineering and Architecture, Zhejiang University, China; Department of Civil Engineering, Hangzhou City University, China.
Ultrasonics
|February 22, 2025
概括
一个新的基于物理学的卷积神经网络 (PICNN) 能够有效地检测和量化多螺栓连接中的螺栓松. 该方法使用引导波能量传输比率来准确监测结构健康状况.
科学领域:
- 结构健康监测 结构健康监测
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 螺栓对于许多行业的结构完整性至关重要.
- 指导波方法在检测螺栓松动方面表现有前途.
- 准确评估多螺栓关节松动性仍然是一个重大挑战.
研究的目的:
- 开发一个基于物理学的卷积神经网络 (PICNN),用于定位和估计螺栓松动的严重程度.
- 为了应对多个螺栓的接头中评估松动性的挑战.
主要方法:
- 采用了SH型的磁强化传感器,采用了pitch-catch配置来捕获传输的波.
- 采用波束变换来导出传输波的时间频谱作为输入.
- 在PICNN中集成了一个反向传播的神经网络,以建模波能量传播,并将其与松严重程度相关联.
- 应用时间和频率掩盖用于数据增强和转移学习,以提高有限样本的准确性.
主要成果:
- 成功估计了八个螺栓的膝关节中螺栓松动的条件.
- 演示了模型定位松并估计其严重程度的能力.
- 验证了数据增强和转移学习技术的有效性.
结论:
- 提出的PICNN提供了一种强大而准确的方法来检测螺栓松和估计重度.
- 该方法为螺栓连接的结构健康监测提供了一个物理可解释的模型.
- 该研究强调了基于物理的机器学习在工程应用中的潜力.
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