电子测量与仪器学报

薛河,赵有俊,王双,孙裕满,王欣玥,张建龙.疲劳裂纹扩展电位信号的最优光滑降噪算法[J].电子测量与仪器学报,2022,36(7):115-124

疲劳裂纹扩展电位信号的最优光滑降噪算法

Optimal smoothing noise reduction algorithm forpotential drop signal of fatigue crack growth

DOI：

英文关键词:VMD algorithm crack potential signal signal reconstruction smooth noise reduction DCPD

基金项目:国家自然科学基金(52075434)、陕西省市场监督管理局科技计划(2021KY10)、陕西省自然科学基础研究计划（2021JM 389）项目资助

作者	单位
薛河	1. 西安科技大学机械工程学院
赵有俊	1. 西安科技大学机械工程学院
王双	1. 西安科技大学机械工程学院
孙裕满	1. 西安科技大学机械工程学院
王欣玥	1. 西安科技大学机械工程学院
张建龙	2. 西安特种设备检验检测院

Author	Institution
Xue He	1. College of Mechanical Engineering, Xi′an University of Science and Technology
Zhao Youjun	1. College of Mechanical Engineering, Xi′an University of Science and Technology
Wang Shuang	1. College of Mechanical Engineering, Xi′an University of Science and Technology
Sun Yuman	1. College of Mechanical Engineering, Xi′an University of Science and Technology
Wang Xinyue	1. College of Mechanical Engineering, Xi′an University of Science and Technology
Zhang Jianlong	2. Xi′an Special Equipment Inspection Institute

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中文摘要:

在采用直流电位降法对疲劳裂纹扩展过程进行实时监测时,由于受到多种噪声干扰使疲劳裂纹扩展电位信号不准确。为了提高其准确性与光滑性,对疲劳裂纹扩展电位信号建立基于变分模态分解(VMD)的最优光滑降噪算法,将裂纹电位信号进行 VMD 分解后,根据各个模态分量的样本熵、相关系数和均方误差等指标,剔除裂纹电位信号中的噪声分量和对含噪的有效模态分量进行降噪处理;然后选择合适的模态分量进行裂纹电位信号的重构,对比不同信号重构方案,选出最优重构信号;最后对最优重构信号建立不同光滑滤波算法,通过对比光滑度、均方误差、信噪比等指标得出最优光滑降噪模型。分析结果表明该算法模型光滑降噪效果良好,降噪误差比为 0. 122 050,提高了监测信号的光滑性与准确性。

英文摘要:

In the process of real-time monitoring of fatigue crack growth by adopting direct current potential drop method (DCPD), multiple noises interference make the potential drop signal of fatigue crack propagation inaccurate. To improve the accuracy and stability of signal, the optimal smoothing noise reduction model based on variational mode decomposition (VMD) is established. The sample entropy, correlation coefficient, and mean square error of each intrinsic mode function (IMF) are calculated to eliminate the interference source in the original signal, and the effective components are selected to reconstruct new potential drop signal. Comparing different signal reconstruction schemes to select the optimal reconstructed signal. Finally, different smoothing noise reduction models are established for the optimal reconstructed signal, and the optimal smoothing noise reduction model is obtained by comparing smoothness, mean square error, signal-to-noise ratio and other indicators. The analysis results show that the smooth noise reduction model has excellent noise reduction effect, the noise reduction error ratio of the optimal smooth model is 0. 122 050, and improves the smoothness and accuracy of the monitoring signal.

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