多工况输气管道泄漏声波信号自适应去噪
Adaptive denoising for leak-induced acoustic in gas pipe under multiple conditions
薛生 1谢晓贤 2郑晓亮 3王强2
作者信息
- 1. 安徽理工大学安全科学与工程学院 淮南 232001;安徽理工大学煤炭安全精准开采国家地方联合工程研究中心 淮南 232001
- 2. 安徽理工大学安全科学与工程学院 淮南 232001
- 3. 安徽理工大学电气与信息工程学院 淮南 232001
- 折叠
摘要
为实现极低信噪比下管道泄漏声波信号去噪,基于多通道信号的相关性,提出使用相关系数矩阵筛选变分模态分解所得模态分量.针对不同工况的泄漏信号,提出不依赖真值的去噪质量评价指标,将其作为多目标灰狼优化算法目标函数,基于Pareto前沿获取变分模态分解的最佳模态数K和惩罚因子η,实现多工况自适应去噪.搭建了输气管道泄漏多工况实验平台,在不同工况、不同输入信号信噪比(-8～4 dB)下验证所提方法的去噪效果.结果表明,该方法能有效抑制噪声,-8 dB时去噪信号信噪比提升2.84 dB以上.对比基于单目标优化的去噪方法,-8 dB下新方法的信噪比和相关系数分别提高了 3.65 dB和31.26％.
Abstract
To achieve denoising of pipe leak acoustic signal under the conditions of extremely low signal-to-noise ratio,based on the signal correlation among multiple channels,a correlation coefficient matrix is presented to determine the modes obtained by using the variational mode decomposition.For the leak signals under different conditions,the quality evaluation index for denoising that does not rely on the real value is presented to use it as the object function of the multi-objective grey wolf optimization algorithm.The best mode number K and penalty factor η of the variational mode decomposition are determined according to the Pareto front,achieving adaptive denoising under multiple conditions.An experimental rig of gas pipe leak under multiple conditions is established to evaluate the effectiveness of the proposed method under multiple conditions and with different signal to noise ratios(-8～4 dB)of input signals.The results show that this method can effectively suppress noises.Even in the case of-8 dB,the signal-to-noise ratio of denoised signals is amplified by more than 2.84 dB.Compared with the method based on single-objective optimization,at-8 dB,the signal-to-noise ratio and correlation coefficient of the new method are increased by 3.65 dB and 31.26％,respectively.
关键词
管道泄漏/自适应去噪/相关系数矩阵/多目标灰狼优化/ParetoKey words
pipe leak/adaptive denoising/correlation coefficient matrix/multi-objective grey wolf optimization/Pareto引用本文复制引用
基金项目
国家自然科学基金重点项目(51934007)
国家重点研发计划(十三五)(2018YFF0301000)
出版年
2024
NETL
NSTL
万方数据