基于VMD分量相对熵分析的压力管道泄漏定位
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摘要
为减小压力管道泄漏定位误差,从而及时处理管道泄漏,预防事故,首先,提出一种基于变分模态分解(VMD)分量相对熵分析的管道泄漏定位方法,采用VMD分解泄漏信号,获得多个分量,用相对熵分析自适应选择方法去除与管道泄漏信号混合的非泄漏信号,获取最佳观测信号;然后,利用管道模态声发射理论与广义S变换互时频分析,减少泄漏声发射信号频散性对定位的影响,获取较准确的泄漏信号的时延与声速;最后,利用管道泄漏时差定位公式计算泄漏位置,实现管道泄漏的精确定位。结果表明:该方法能够实现管道泄漏精确定位,与直接互时频分析方法以及相关系数分析法相比,定位精度明显提高。
In order to reduce pipeline leakage positioning error,so as to timely deal with pipeline leakage and prevent accidents,a pipeline leakage location method based on relative entropy analysis of VMD components was worked out. In the process of working out it,VMD was used to decompose the leakage signal into multiple components,the relative entropy analysis adaptive selection method was used to remove the non-leakage signals mixed with the pipeline leakage signal and obtain the best observation signal. Then,pipeline modal acoustic emission theory and generalized S-transformation time-frequency analysis were used to reduce the effect of the dispersion of the leak acoustic emission signal on location,and the delay and sound velocity of the leak signal were obtained accurately. Finally,the leak location was calculated by the pipeline leakage time difference positioning formula to achieve accurate positioning of the pipeline leakage. The results show that the method can realize the precise location of pipeline leakage points,and that the positioning accuracy is significantly improved compared with those by the direct mutual time-frequency analysis method and the correlation coefficient analysis method.
In order to reduce pipeline leakage positioning error,so as to timely deal with pipeline leakage and prevent accidents,a pipeline leakage location method based on relative entropy analysis of VMD components was worked out. In the process of working out it,VMD was used to decompose the leakage signal into multiple components,the relative entropy analysis adaptive selection method was used to remove the non-leakage signals mixed with the pipeline leakage signal and obtain the best observation signal. Then,pipeline modal acoustic emission theory and generalized S-transformation time-frequency analysis were used to reduce the effect of the dispersion of the leak acoustic emission signal on location,and the delay and sound velocity of the leak signal were obtained accurately. Finally,the leak location was calculated by the pipeline leakage time difference positioning formula to achieve accurate positioning of the pipeline leakage. The results show that the method can realize the precise location of pipeline leakage points,and that the positioning accuracy is significantly improved compared with those by the direct mutual time-frequency analysis method and the correlation coefficient analysis method.
引文
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[14]张蒙,朱永利,张宁,等.基于变分模态分解和多尺度排列熵的变压器局部放电信号特征提取[J].华北电力大学学报:自然科学版,2016,43(6):31-37.ZHANG Meng,ZHU Yongli,ZHANG Ning,et al. Feature txtraction of transformer partial discharge signals based on varitional mode decomposition and multi-scale permutation entropy[J].Journal of North China Electric Power University,2016,43(6):31-37.
[2]郝永梅,徐明,李秀中,等.基于广义概念的城市燃气管道泄漏精确定位[J].工业安全与环保,2016,42(1):80-84.HAO Yongmei,XU Ming,LI Xiuzhong,et al. The precise location of city gas pipeline leak based on the generalized concept[J]. Industrial Safety and Environmental Protection,2016,42(1):80-84.
[3] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing,2013,61(16):3 999-4 010.
[4]刘翠伟,李玉星,孟令雅,等.基于希尔伯特黄变换的输气管道泄漏音波时频特性分析[J].振动与冲击,2014,33(16):42-49.LIU Cuiwei,LI Yuxing,MENG Lingya,et al. Time-frequency analysis of acoustic leakage signal for natural gas pipelines based on Hilbert-Huang transform[J]. Journal of Vibration and Shock,2014,33(16):42-49.
[5]孙洁娣,肖启阳,温江涛,等.局域均值分解分析的管道泄漏孔径识别及定位[J].仪器仪表学报,2014,35(12):2 835-2 842.SUN Jiedi,XIAO Qiyang,WEN Jiangtao,et al. Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM[J]. Measurement,2014,35(12):434-443.
[6]李健,封超.基于EEMD和二次相关法的管道泄漏定位检测[J].纳米技术与精密工程,2017,15(5):372-377.LI Jian,FENG Chao. Pipeline leak location detection based on EEMD and second correlation[J]. Nanotechnology and Precision Engineering,2017,15(5):372-377.
[7]刘炜,刘宏昭.检测与定位管道泄漏的图像处理方法研究[J].控制工程,2014,21(2):294-297.LIU Wei,LIU Hongzhao. Application of image processing technology on detection and location of pipeline leak[J].Control Engineering of China,2014,21(2):294-297.
[8] MOHANT Y,GUPAT K K,RAJU K S. Bearing fault analysis using variational mode decomposition[C]. 9thIEEE International Conference on Industrial and Information Systems. ICIIS,2014,4(2):1-6.
[9] WANG Yanxue,MARKERT R,XIANG Jiawei,et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J]. Mechanical Systems&Signal Processing,2015,60/61:243-251.
[10]韩中合,朱霄珣,李文华.基于K-L散度的EMD虚假分量识别方法研究[J].中国电机工程学报,2012,32(11):112-117.HAN Zhonghe,ZHU Xiaoxun,LI Wenhua. A false component identification method of EMD based on Kullback-Leibler divergence[J]. Proceedings of the CSEE,2012,32(11):112-117.
[11]罗裳.基于KL熵矩阵近似问题的研究[D].广州:华南理工大学,2014.LUO Shang. Study on matrix approximation problems with KL entropy[D]. Guangzhou:South China University of Technology,2014.
[12]李帅永,王鹏飞,严冬,等.气体管道泄漏模态声发射时频定位方法[J].仪器仪表学报,2016,37(9):2 068-2 075.LI Shuaiyong,WANG Pengfei,YAN Dong,et al. Leak location in gas pipelines with time-frequency analysis of modal acoustic emission using smooth pseudo Wigner-Ville distribution[J]. Chinese Journal of Scientific Instrument,2016,37(9):2 068-2 075.
[13]杨阳.广义S变换时频分析的应用研究[D].哈尔滨:哈尔滨工程大学,2011.YANG Yang. Application research of generalized S transform time frequency analysis[D]. Harbin:Harbin Engineering University,2011.
[14]张蒙,朱永利,张宁,等.基于变分模态分解和多尺度排列熵的变压器局部放电信号特征提取[J].华北电力大学学报:自然科学版,2016,43(6):31-37.ZHANG Meng,ZHU Yongli,ZHANG Ning,et al. Feature txtraction of transformer partial discharge signals based on varitional mode decomposition and multi-scale permutation entropy[J].Journal of North China Electric Power University,2016,43(6):31-37.