基于结构相似度的管道泄漏检测定位法
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摘要
从时间序列角度出发,提出一种基于结构相似度准则的输油管道泄漏检测定位方法。首先采用局部投影降噪法去除动态压力波信号噪声,其次验证并选取固定长度的正常动态压力波信号子序列作为模板序列,并选取相同长度的现场采集动态压力波信号作为被匹配序列,构造两子序列信号对应功率谱,然后计算其归一化功率谱之间的结构相似度;最后,根据结构相似度判定输油管道是否存在泄漏现象,若发生泄漏,则自动确定泄漏位置。研究结果表明:该方法可有效判定和定位输油管道泄漏。
From the perspective of time series, a pipeline leak detection and location method based on structural similarity criteria was proposed. Firstly, the dynamic pressure wave signal noise was removed by local projection noise reduction method; secondly, the template sequence was validated and selected through the normal dynamic pressure wave signal subsequence with fixed length, and the matched sequence was selected by on-site acquisition dynamic pressure wave signal at the same length with which two sub-sequence signal corresponding power spectrums were calculated, and then the structural similarities between the normalized power spectrum were computed; finally, pipeline leaks based on the similarity of structure were determined if leaks occurred and the location of the leak was automatically determined. The results show that this method can determine and locate pipeline leak effectively.
From the perspective of time series, a pipeline leak detection and location method based on structural similarity criteria was proposed. Firstly, the dynamic pressure wave signal noise was removed by local projection noise reduction method; secondly, the template sequence was validated and selected through the normal dynamic pressure wave signal subsequence with fixed length, and the matched sequence was selected by on-site acquisition dynamic pressure wave signal at the same length with which two sub-sequence signal corresponding power spectrums were calculated, and then the structural similarities between the normalized power spectrum were computed; finally, pipeline leaks based on the similarity of structure were determined if leaks occurred and the location of the leak was automatically determined. The results show that this method can determine and locate pipeline leak effectively.
引文
[1]ELLUL I R.Advances in pipeline leak detection techniques[J].Pipes and Pipelines International,1989,34(3):7-12.
[2]ELLUL I R.Advance in pipeline leak detection[J].Pipeline Engineering,ASME,1991,34:15-19.
[3]GAO Y,BRENNAN M J,JOSEPH P F,et al.A model of the correlation function of leak noise in buried plastic pipes[J].Journal of Sound and Vibration,2004,277(1/2):133-148.
[4]葛传虎,王桂增,叶昊,等.基于广义相关分析的泄漏定位[J].信息与控制,2009,38(2):194-198,205.GE Chuanhu,WANG Guizeng,YE Hao,et al.Leak location based on generalized correlation analysis[J].Information and Control,2009,38(2):194-198,205.
[5]杨进,文玉梅,李平.基于相关分析和近似熵的管道泄漏声信号特征提取及辨识方法[J].仪器仪表学报,2009,30(2):272-279.YANG Jin,WEN Yumei,LI Ping.Feature extraction and identification of leak acoustic signal in water distribution[J].Journal of Projectiles,Rockets,Missiles,and Guidance,2009,30(2):272-279.
[6]毛海杰,李炜,冯小林.基于EMD和相关分析的管道泄漏定位检测研究[J].科学技术与工程,2010,10(19):4628-4632.MAO Haijie,LI Wei,FENG Xiaolin.Investigation of method for pipeline leak detection and location based on EMD and cross-correlation[J].Science Technology and Engineering,2010,10(19):4628-4632.
[7]张景川,曾周末,张兆伟.基于小波包分解的多尺度互相关管道定位方法[J].纳米技术与精密工程,2011,9(2):162-167.ZHANG Jingchuan,ZENG Zhoumo,ZHANG Zhaowei.A location method for pipeline based on wavelet packet decomposition with multi-scale cross-correlation analysis[J].Nanotechnology and Precision Engineering,2011,9(2):162-167.
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[11]王明达,张来斌,梁伟,等.基于独立分量分析和支持向量机的管道泄漏识别方法[J].石油学报,2010,34(1):659-663.WANG Mingda,ZHANG Laibin,LIANG Wei,et al.Pipeline leakage detection method based on independent component analysis and support vector machine[J].Acta Petroleum Sinica,2010,34(1):659-663.
[12]赵伟,曾周末,靳世久,等.基于归一化小波能量系数的管道安全识别算法[J].纳米技术与精密工程,2012,10(2):147-153.ZHAO Wei,ZENG Zhoumo,JIN Shijiu,et al.Recognition algorithm based on normalized wavelet energy coefficients for the safety of pipeline[J].Nanotechnology and Precision Engineering,2012,10(2):147-153.
[13]李炜,张美玲,毛海杰.基于模糊RBF神经网络的管道泄漏检测方法[J].兰州理工大学学报,2009,35(6):79-83.LI Wei,ZHANG Meiling,MAO Haijie.Detection method of pipeline leak based on fuzzy RBF neural network[J].Journal of Lanzhou University of Technology,2009,35(6):79-83.
[14]董东,王桂增,方崇智.基于Kullback信息测度的长输管线的故障诊断[J].清华大学学报(自然科学版),1989,29(4):37-44.DONG Dong,WANG Guizeng,FANG Chongzhi.Fault diagnosis for transort pipelines based on Kullbaclk information[J].Journal of Tsinghua University(Science and Technology),1989,29(4):37-44.
[15]WANG Guizeng,DONG Dong,FANG Chongzhi.Leak detection for transport pipelines based on autoregressive modeling[J].IEEE Transaction on Instrument and Measure,1993,42(1):68-69.
[16]THEODOR D P.Detection and diagnosis of model parameter and noise variance changes using sliding windows approach[J].Recent Patents on Signal Processing,2011,1(1):80-87.
[17]LIU S,YAMADA M,COLLIER N,et al.Change-point detection in time-series data by relative density-ratio estimation[J].Neural Networks,2013,43:72-83.
[18]ZHOU W,BOVIK A C,SHEIKH H R.Image quality assessment:from error visibility to structural similarity[J].IEEE Transaction on Image Processing,2004,13(4):600-612.
[19]叶迎春,张来斌,梁伟.天然气管道泄漏音波序列局部投影降噪研究[J].中国石油大学学报(自然科学版),2010,34(1):118-124.YE Yingchun,ZHANG Laibin,LIANG Wei.Study of noise reduction of natural gas pipe line leakage acoustic series by local projection[J].Journal of China University of Petroleum,2010,34(1):118-124.
[20]SMITH L A.Intrinstic limits on dimension calculations[J].Phys Lett A,1988,133(6):283-288.
[21]SHI Jingzhuo,ZHAO Fujie,SHEN Xiaoxi,et al.Chaotic operation and chaos control of travelling wave ultrasonic motor[J].Ultrasonics,2013,53(6):1112-1123.
[22]郭兴明,张文英,袁志会,等.基于EMD关联维数和多重分形谱的心音识别[J].仪器仪表学报,2014,35(4):827-833.GUO Xingming,ZHANG Wenying,YUAN Zhihui,et al.Heart sound recognition based on EMD correlation dimension and multi-fractals spectrum[J].Chinese Journal of Scientific Instrument,2014,35(4):827-833.
[2]ELLUL I R.Advance in pipeline leak detection[J].Pipeline Engineering,ASME,1991,34:15-19.
[3]GAO Y,BRENNAN M J,JOSEPH P F,et al.A model of the correlation function of leak noise in buried plastic pipes[J].Journal of Sound and Vibration,2004,277(1/2):133-148.
[4]葛传虎,王桂增,叶昊,等.基于广义相关分析的泄漏定位[J].信息与控制,2009,38(2):194-198,205.GE Chuanhu,WANG Guizeng,YE Hao,et al.Leak location based on generalized correlation analysis[J].Information and Control,2009,38(2):194-198,205.
[5]杨进,文玉梅,李平.基于相关分析和近似熵的管道泄漏声信号特征提取及辨识方法[J].仪器仪表学报,2009,30(2):272-279.YANG Jin,WEN Yumei,LI Ping.Feature extraction and identification of leak acoustic signal in water distribution[J].Journal of Projectiles,Rockets,Missiles,and Guidance,2009,30(2):272-279.
[6]毛海杰,李炜,冯小林.基于EMD和相关分析的管道泄漏定位检测研究[J].科学技术与工程,2010,10(19):4628-4632.MAO Haijie,LI Wei,FENG Xiaolin.Investigation of method for pipeline leak detection and location based on EMD and cross-correlation[J].Science Technology and Engineering,2010,10(19):4628-4632.
[7]张景川,曾周末,张兆伟.基于小波包分解的多尺度互相关管道定位方法[J].纳米技术与精密工程,2011,9(2):162-167.ZHANG Jingchuan,ZENG Zhoumo,ZHANG Zhaowei.A location method for pipeline based on wavelet packet decomposition with multi-scale cross-correlation analysis[J].Nanotechnology and Precision Engineering,2011,9(2):162-167.
[8]封皓,靳世久,曾周末,等.基于三阶累积量及自适应滤波时延估计的管道定位方法[J].振动与冲击,2011,30(9):207-210.FENG Hao,JIN Shijiu,ZENG Zhoumo,et al.Location method for pipeline vibration based on third-order cumulant and adaptive filter time delay estimation[J].Journal of Vibration and Shock,2011,30(9):207-210.
[9]刘一辛,吴臣勇.输气管道泄漏定位的复相关迭代算法[J].科技创新导报,2012(15):51.LIU Yixin,WU Chenyong.Complex correlation iterated algorithm of Transport pipe leak location[J].Science and Technology Innovation Herald,2012(15):51.
[10]崔谦,靳世久,李一博.模糊聚类分析方法在管道泄漏检测系统中的应用研究[J].电子测量与仪器学报,2006,20(2):60-62.CUI Qian,JIN Shijiu,LI Yibo.Application and research of fuzzy cluster analysis in oil pipeline leak detection system[J].Journal of Electronic Measurement and Instrument,2006,20(2):60-62.
[11]王明达,张来斌,梁伟,等.基于独立分量分析和支持向量机的管道泄漏识别方法[J].石油学报,2010,34(1):659-663.WANG Mingda,ZHANG Laibin,LIANG Wei,et al.Pipeline leakage detection method based on independent component analysis and support vector machine[J].Acta Petroleum Sinica,2010,34(1):659-663.
[12]赵伟,曾周末,靳世久,等.基于归一化小波能量系数的管道安全识别算法[J].纳米技术与精密工程,2012,10(2):147-153.ZHAO Wei,ZENG Zhoumo,JIN Shijiu,et al.Recognition algorithm based on normalized wavelet energy coefficients for the safety of pipeline[J].Nanotechnology and Precision Engineering,2012,10(2):147-153.
[13]李炜,张美玲,毛海杰.基于模糊RBF神经网络的管道泄漏检测方法[J].兰州理工大学学报,2009,35(6):79-83.LI Wei,ZHANG Meiling,MAO Haijie.Detection method of pipeline leak based on fuzzy RBF neural network[J].Journal of Lanzhou University of Technology,2009,35(6):79-83.
[14]董东,王桂增,方崇智.基于Kullback信息测度的长输管线的故障诊断[J].清华大学学报(自然科学版),1989,29(4):37-44.DONG Dong,WANG Guizeng,FANG Chongzhi.Fault diagnosis for transort pipelines based on Kullbaclk information[J].Journal of Tsinghua University(Science and Technology),1989,29(4):37-44.
[15]WANG Guizeng,DONG Dong,FANG Chongzhi.Leak detection for transport pipelines based on autoregressive modeling[J].IEEE Transaction on Instrument and Measure,1993,42(1):68-69.
[16]THEODOR D P.Detection and diagnosis of model parameter and noise variance changes using sliding windows approach[J].Recent Patents on Signal Processing,2011,1(1):80-87.
[17]LIU S,YAMADA M,COLLIER N,et al.Change-point detection in time-series data by relative density-ratio estimation[J].Neural Networks,2013,43:72-83.
[18]ZHOU W,BOVIK A C,SHEIKH H R.Image quality assessment:from error visibility to structural similarity[J].IEEE Transaction on Image Processing,2004,13(4):600-612.
[19]叶迎春,张来斌,梁伟.天然气管道泄漏音波序列局部投影降噪研究[J].中国石油大学学报(自然科学版),2010,34(1):118-124.YE Yingchun,ZHANG Laibin,LIANG Wei.Study of noise reduction of natural gas pipe line leakage acoustic series by local projection[J].Journal of China University of Petroleum,2010,34(1):118-124.
[20]SMITH L A.Intrinstic limits on dimension calculations[J].Phys Lett A,1988,133(6):283-288.
[21]SHI Jingzhuo,ZHAO Fujie,SHEN Xiaoxi,et al.Chaotic operation and chaos control of travelling wave ultrasonic motor[J].Ultrasonics,2013,53(6):1112-1123.
[22]郭兴明,张文英,袁志会,等.基于EMD关联维数和多重分形谱的心音识别[J].仪器仪表学报,2014,35(4):827-833.GUO Xingming,ZHANG Wenying,YUAN Zhihui,et al.Heart sound recognition based on EMD correlation dimension and multi-fractals spectrum[J].Chinese Journal of Scientific Instrument,2014,35(4):827-833.