基于局部平均分解-奇点值分解和加权空间光谱的混凝土块的内部缺陷检测算法
1Research Institute of Optical Communication, School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种结合局部平均分解-单数值分解 (LMD-SVD) 和加权空间光谱 (WSSE) 的新算法,用于使用激光多普勒振动计 (LDV) 改进混凝土内部缺陷检测. 该方法有效地识别了混凝土结构中的浅缺陷位置和形状.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 非破坏性测试是指非破坏性测试.
背景情况:
- 激光多普勒振动计 (LDV) 对于混凝土内部缺陷检测至关重要.
- 由于混凝土的异质性和表面粗性,LDV信号中的低信号噪声比 (SNR) 阻碍了精确的缺陷表征.
- 现有的方法难以准确地识别缺陷信号特征.
研究的目的:
- 开发一种先进的算法,用于使用LDV.增强混凝土内部缺陷检测.
- 为了应对低SNR和复杂材料特性在混凝土检查中的挑战.
- 准确地定位和描述混凝土结构中的内部缺陷.
主要方法:
- 一个新的算法集成局部平均分解-奇点值分解 (LMD-SVD) 信号消噪.
- 权重空间光谱 (WSSE) 的应用来分析扫描平面中的频率分布.
- 根据估计的共振频率的模态振动模式对缺陷的描述.
主要成果:
- LMD-SVD算法有效地减少了LDV信号中的噪声干扰.
- WSSE分析显示缺陷共振频率的能量分布不均,使其能够估计.
- 拟议的算法成功地确定了混凝土块中模拟的浅缺陷的位置和形状.
结论:
- 结合LMD-SVD和WSSE算法,通过LDV显著提高了通过混凝土内部缺陷检测的准确性.
- 该方法为在现实工程场景中识别浅缺陷提供了实用和有效的解决方案.
- 该研究强调了先进的信号处理技术在非破坏性评估混凝土结构方面的潜力.
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