基于声学排放检测技术的钢桥甲板疲劳微裂标识别的调查
Li Jiaqing1, Song Fei2, Xiao Zidong3
1Guangdong Shengxiang Traffic Engineering Testing Co. Ltd., Guangzhou, China.
PloS one
|April 29, 2025
概括
本研究引入了一种结合K-单值分解 (K-SVD) 和卷积神经网络 (CNN) 的新方法,用于精确检测钢桥上的疲劳裂. 该方法显著改善了使用声辐射信号识别结构损坏的方法.
科学领域:
- 结构健康监测 结构健康监测
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 由于交通量增加和使用寿命延长,钢桥甲板的疲劳损伤越来越令人担忧.
- 传统的检测方法与实际工程所需的准确性和响应性作斗争.
- 在裂开始和传播期间的声波发射 (AE) 信号通常是非静止的.
研究的目的:
- 为了增强声辐射 (AE) 信号处理,用于检测钢桥甲板的疲劳裂.
- 开发一种更准确,更快速的方法来识别结构损坏.
- 将词典学习与深度学习相结合,以改进AE信号分析.
主要方法:
- 使用K-单数值分解 (K-SVD) 作为适应性波器来消除AE信号并提取结构特征.
- 使用卷积神经网络 (CNN) 来分类K-SVD处理的AE信号.
- 在受损和未受损状态的AE信号上训练并测试了集成的K-SVD-CNN模型.
主要成果:
- 实现了高识别精度:93.64% (训练) 和92.56% (测试) 损坏的AE信号.
- 获得了优异的识别准确度:95.32% (训练) 和94.27% (测试) 对于未受损的AE信号.
- 证明了K-SVD在改善AE信号分类CNN性能方面的有效性.
结论:
- 集成的K-SVD-CNN方法提供了一个可扩展和准确的解决方案,用于实时检测桥梁检查中的裂.
- 该方法为提高钢桥疲劳裂检测提供了理论基础和实际指导.
- 未来的优化策略可以解决计算强度的问题,使该方法在自动化桥梁检查中的实际应用中变得可行.
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