基于自编码器和BP神经网络的声波发射波形选方法
Shunli Jiang1, Kangpei Zheng2, Jinghui Jv3
1School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, China.
The Review of scientific instruments
|September 2, 2025
概括
这项研究引入了一种自编码器和反向传播的神经网络,用于过声辐射 (AE) 信号,改善噪声分离,提高混凝土结构健康监测的准确性.
科学领域:
- 工程
- 材料科学
- 信号处理
背景情况:
- 声辐射 (AE) 信号叠加使准确的分析变得复杂.
- 有效的降噪对于可靠的结构健康监测至关重要.
研究的目的:
- 开发和评估用于AE信号分离的新型波形过方法.
- 在混凝土结构中提高AE源定位的准确性.
主要方法:
- 使用自动编码器与反向传播神经网络 (BP) 进行信号过.
- 使用笔断实验和数值模拟验证了模型.
主要成果:
- 自动编码器-BP神经网络模型实现了高分类精度:AE信号96%和噪声98%
- 过的数据显著提高了AE源的定位精度.
结论:
- 拟议的AE信号处理方法有效地将AE信号与噪声分开.
- 这种技术为加强混凝土结构的安全监测提供了巨大的潜力.
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