李帅永,韩明秀,文井辉.基于 VMD-SVD 自优化的管道微泄漏信号增强方法[J].电子测量与仪器学报,2021,35(12):68-78 |
基于 VMD-SVD 自优化的管道微泄漏信号增强方法 |
Weak signal enhancement based on self-optimizing VMD-SVDfor leak location in water-supply pipeline |
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DOI: |
中文关键词: 遗传算法 奇异值分解 变分模态分解 参数自优化 信号增强 |
英文关键词:genetic algorithm singular value decomposition variational mode decomposition parameter-optimized signal enhancement |
基金项目:国家重点研发计划项目(2019YFB2005900)、国家自然科学基金(61703066)、重庆市基础研究与前沿探索项目(cstc2018jcyjAX0536)、重庆市技术创新与应用发展专项(cstc2018jszx cyztzxX0028,cstc2019jscx fxydX0042,cstc2019jscx zdztzxX0053)资助 |
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中文摘要: |
针对复杂环境噪声下管道微泄漏特征难以提取的问题,提出基于变分模态分解-奇异值分解(VMD-SVD)自优化的管道
微泄漏信号增强方法。 首先,采用遗传迭代算法对 VMD 参数对[α,K]进行自适应优化,采用奇异值峭度差频谱对 SVD 重构阶
次进行自适应优化;然后,采用参数优化的 VMD 对泄漏信号进行分解,并采用峭度分析法对分解的模态分量进行筛选并重构;
最后,采用阶次优化的 SVD 对重构信号进行非线性滤波,从而提高微泄漏信号的信噪比。 仿真与实验结果表明,信号增强方法
使仿真信号的信噪比提高了 9. 32 dB,使管道微泄漏信号的相关性提高了 5. 92 倍,使互相关泄漏相对定位误差减少了 14. 34%。 |
英文摘要: |
Aiming at the problem that it is difficult to extract the early characteristics of pipeline micro-leakage under complex
environmental noise, this paper proposed a method based on variational mode decomposition-singular value decomposition(VMD-SVD)
self-optimizing pipeline microleakage signal enhancement method. Firstly, the genetic iterative algorithm was used to optimize the VMD
parameters [α,k] , and the singular value kurtosis difference spectrum was used to optimize the reconstruction order of SVD. Then, the
leakage signals were decomposed by VMD with optimized parameters, and the decomposed modal components were screened and
reconstructed by kurtosis analysis. Finally, order optimized SVD is used to nonlinear filter the reconstructed signals, so as to improve the
signal-to-noise ratio (SNR) of micro-leakage signals. Simulation and experimental results show that: the signal enhancement method
proposed in this paper improves the signal-to-noise ratio of simulation signals by 9. 32 dB, the correlation of pipeline micro-leakage
signals has been increased by 5. 92 times, and the relative positioning error of cross-correlation leakage is reduced by 14. 34%. |
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