基于网络控制的长输油管道泄漏检测与定位技术研究
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摘要
原油管道运输中的泄漏事故，不仅损失原油和污染环境，还有可能带来重大的人身伤亡。近些年来，管道泄漏事故频繁发生，为保障管道安全运行和将泄漏事故造成的危害减少到最小，需要研究泄漏检测技术以获得更高的泄漏检测灵敏度和更准确的泄漏点定位精度。管道泄漏检测是多领域、跨学科的课题，涉及到管道流体力学、热力学、传感技术、微弱信号检测、信号处理等多个学科，本文对泄漏检测及定位技术进行了初步的研究，主要进行了以下几方面的工作：
1．分析了负压波泄漏定位方法的两项关键技术，提出了消除首末、端压力传感器动态响应时间差的方法，采用GPS来统一首末端数据采集系统的系统时间；使定位更加准确。利用信号奇异点与小波变换模极大值在多尺度上变化对应的性质，将离散小波变换应用到获取压力波信号序列特征奇异点中，可以对泄漏点进行定位。因为现场采集的数据中含有大量噪声，我们采取小波门限去除噪声干扰的方法，可以很好的消除噪声干扰。在实际应用中取得了良好的效果。
2．本文将基于小波包分析的“能量—故障识别”故障诊断方法引入管道运行状态监测技术，提出了反映泄漏压力信号特性的特征向量指标。该方法对信号进行小波包分解，通过分析在每个分解节点上重构的新序列，就能根据各频带内的特征向量对压力变化原因进行识别。
3．本文应用正常情况下的特征向量与泄漏情况的特征向量进行比较，并且采用阀值的方法判断泄漏故障的发生。本文还采用RBF神经网络的方法对对管道的运行状态进行在线识别，可以检测出管道的泄漏，仿真结果表明效果较好。
4．最后应用网络控制理论，建立了一套输油管线泄漏检测定位的网络控制系统，并给出了相应的网络控制模型和基本的软硬件实现步骤。
The leakage accidents in pipelines transportation not only cause great loss of material, but also pollute the environment; what's more, it may lead to serious casualty. Leakage accidents in pipeline transportation occur frequently in recent years. In order to guarantee the pipelines work safely and minimize the losses caused by leakage accidents, it is necessary to study leakage detecting technology to raise the sensitivity of detection and accuracy of localization. Pipeline leakage detection is a multi-field object, which involved many subjects, such as pipeline hydrodynamics, thermodynamics, sensor technology, subtle signal detection and signal processing. This search on pipeline leakage detection and localization is carried out in this dissertation The main efforts accomplished can be summed up as the following aspects:
1. Two crucial technologies in instantaneous negative pressure wave method are analyzed in this dissertation, then a means is presented which can eliminate the dynamic response time diversity between pressure sensors in the beginning and end of the pipeline, by adopting GPS to unify the system time between data collecting systems in two ends. Taking account of the corresponding characteristic of the signal singularity and wavelet transform module maximum in multi-scale changing, the discrete wavelet transform is applied to get the featured inflection point of negative pressure wave. Because data that scene gather contain a large number of noises, we use wavelet valve method denoise interferes, Have made the good result in practical application
2. "Energy -fault recognition", a fault diagnosis method based on wavelet packets analysis for the transport pipeline supervising system is proposed in this dissertation. Eigenvector target is presented which reflect the characteristic of leakage pressure signal. The signal is wavelet packets decomposed in this method. By analyzing the new sequences reconstructed on every decomposition note, the fault then can be recognized according to the eigenvectors in each frequency band.
3. Use characteristic vector quantity of normal situation with leak characteristic vector quantity of situation compare, adopting valve method determinant the leakage of pipelines. The method that this text still adopts RBF neural network discerns the state of pipeline online, Can measure out the leakage of pipeline , the simulation result indicates that the result is better.
4. Use network control theory, set up one petroleum pipeline leak network control system of leakage detection and localization, corresponding network control models and basic software and hardware had been put out.
1．分析了负压波泄漏定位方法的两项关键技术，提出了消除首末、端压力传感器动态响应时间差的方法，采用GPS来统一首末端数据采集系统的系统时间；使定位更加准确。利用信号奇异点与小波变换模极大值在多尺度上变化对应的性质，将离散小波变换应用到获取压力波信号序列特征奇异点中，可以对泄漏点进行定位。因为现场采集的数据中含有大量噪声，我们采取小波门限去除噪声干扰的方法，可以很好的消除噪声干扰。在实际应用中取得了良好的效果。
2．本文将基于小波包分析的“能量—故障识别”故障诊断方法引入管道运行状态监测技术，提出了反映泄漏压力信号特性的特征向量指标。该方法对信号进行小波包分解，通过分析在每个分解节点上重构的新序列，就能根据各频带内的特征向量对压力变化原因进行识别。
3．本文应用正常情况下的特征向量与泄漏情况的特征向量进行比较，并且采用阀值的方法判断泄漏故障的发生。本文还采用RBF神经网络的方法对对管道的运行状态进行在线识别，可以检测出管道的泄漏，仿真结果表明效果较好。
4．最后应用网络控制理论，建立了一套输油管线泄漏检测定位的网络控制系统，并给出了相应的网络控制模型和基本的软硬件实现步骤。
The leakage accidents in pipelines transportation not only cause great loss of material, but also pollute the environment; what's more, it may lead to serious casualty. Leakage accidents in pipeline transportation occur frequently in recent years. In order to guarantee the pipelines work safely and minimize the losses caused by leakage accidents, it is necessary to study leakage detecting technology to raise the sensitivity of detection and accuracy of localization. Pipeline leakage detection is a multi-field object, which involved many subjects, such as pipeline hydrodynamics, thermodynamics, sensor technology, subtle signal detection and signal processing. This search on pipeline leakage detection and localization is carried out in this dissertation The main efforts accomplished can be summed up as the following aspects:
1. Two crucial technologies in instantaneous negative pressure wave method are analyzed in this dissertation, then a means is presented which can eliminate the dynamic response time diversity between pressure sensors in the beginning and end of the pipeline, by adopting GPS to unify the system time between data collecting systems in two ends. Taking account of the corresponding characteristic of the signal singularity and wavelet transform module maximum in multi-scale changing, the discrete wavelet transform is applied to get the featured inflection point of negative pressure wave. Because data that scene gather contain a large number of noises, we use wavelet valve method denoise interferes, Have made the good result in practical application
2. "Energy -fault recognition", a fault diagnosis method based on wavelet packets analysis for the transport pipeline supervising system is proposed in this dissertation. Eigenvector target is presented which reflect the characteristic of leakage pressure signal. The signal is wavelet packets decomposed in this method. By analyzing the new sequences reconstructed on every decomposition note, the fault then can be recognized according to the eigenvectors in each frequency band.
3. Use characteristic vector quantity of normal situation with leak characteristic vector quantity of situation compare, adopting valve method determinant the leakage of pipelines. The method that this text still adopts RBF neural network discerns the state of pipeline online, Can measure out the leakage of pipeline , the simulation result indicates that the result is better.
4. Use network control theory, set up one petroleum pipeline leak network control system of leakage detection and localization, corresponding network control models and basic software and hardware had been put out.
引文
[1] 藏铁军，藏天红．我国输油管道的发展概况．管道技术与设备，1998，(4)：1～4
[2] FurnessR.A. Development in Pipeline Instrumentation. Measurement and Control,1987,20(1): 7～15
[3] 陈华波，涂亚庆．输油管道泄漏检测方法综述．管道技术与设备，2000，(1)：38～41
[4] 夏海波，张来斌，王朝辉．国内外油气管道泄漏检测技术的发展现状．油气储运，2001 20(1)：1～5
[5] M.S.Yoon et al. Leak Detection Performance Specification. Pipeline Engineering. ASME 1991,34:21～26
[6] [日]森村正直，正崎弘郎主编．黄香泉译．传感器技术．北京：科学出版社，1988，405
[7] M.Nagat et al. Sensitive Liquid Sensor for Long Distance Leak Detection. SPIE,1367
[8] ELLul I.R. Advances in pipeline Leak Detection Techniques. Pipes and Pipelines International, 1989,34(3): 7～12
[9] Andy Wike. SCADA-Based Leak Detection System. Pipeline and Gas Joural, 1986, 213(6): 20
[10] 冉启文．小波分析方法及应用．哈尔滨：哈尔滨工业大学出版社，1995，5～12
[11] 刘贵忠，邸双亮．小波分析及应用．西安：西安电子科技大学，1993，6～34
[12] 秦前清，杨宗凯．实用小波分析．西安：西安电子科技大学出版社，1994，23～56
[13] 程正兴．小波分析算法与应用．西安：西安交通大学出版社，1998，32-65
[14] 杨福生．小波分析的工程分析与应用．北京：科学出版社，1999，24～35
[15] 李爱萍，段利国．小波分析在信号将消躁处理中的应用．太原理工大学学报，2001．Vol．32 No．1
[16] 李传庆，徐敏，张曙．基于小波分析的信号消躁法．应用科技，2003．Vol．30 No．2
[17] DAUBECHIES I。The wavelet transform, time-frequency localization and signal analysis [J]。 IEEE Trans, 1990,36 (5): 961～1005
[18] 何风华．小波分析在信号消躁中应用．自动检测技术，2002，Vol．21，No．6
[19] 胡昌，张君波，夏军，等．基于MATLAB的系统分析与设计——小波分析．西安：西安电子科技大学出版社，2000，223～246
[20] Wasserman P D. Radial Basis-Function Networks. in Advanced Methods in Neural Computing, Van Nostrand Reinhold, New York, U. S. A 1993,147～176
[21] Girosi F, Poggio T. Networks and the Best Approximation Property. Neuro-Control Systems:Theory and Applications, Eds. By Gupta M M and Rao D H, IEEE Press, 1994,257～264
[22] Poggio T, Girosi F. Networks for Approximation and Learning, Proc. Of IEEE, 1990, 78(9):1481～1497
[23] Leonard, J. A., Kramer, M. A. and Ungar, L. H. ,Using Radial Basis Functions to Approximate A Function and Its Error Bounds, IEEE Trans. On NNs, 1992,3(4):624～257
[24] Park J, Sandberg I W. Universal Approximation Using Radial Basis Function Network, Neural Computation, 1991,3:246～257
[25] 王洪元，史国栋．人工神经网络技术及其应用．北京：中国石化出版社，2003，61～69
[26] Narayan S Shankar,Peterson Allen M, Narasimha Medihally J. Transform domain LMS algorithm[J]. IEEE Trans Acoustics,Speech and Signal Processing, 1983,31(3): 609～615
[27] Mallat S, Hwang W L. Singularity detection and processing with wavelet. IEEE Trans. On Information Theory, 1992,38 (2): 617～643
[28] 周东华，叶银忠．现代故障诊断于容错控制．北京：清华大学出版社，2000 132～135
[29] 叶昊，王桂增，方崇智等．小波变换在故障诊断中的应用．自动化学报，1997，23(6)：736～740
[30] 喻文焕，金振东，刘峡等．信号奇异性的检测及应用．系统工程理论以应用，2000，3：125～129
[31] 陶洛文，方崇智等．以辨识为基础的长输管线故障定位．清华大学学报，1986，26(2)：69～75
[32] 王海生，叶昊，王桂增．基于小波分析的输油管道泄漏检测．信息与控制，2002．Vol．31，No．5
[33] 王立宁，靳世久等．热输原油管道瞬态压力波法泄漏点定位研究．石油学报，2000 21(4)：93～96
[34] 董绪荣，张守信等．GPS／INS组合导航定位及其应用．国防科技大学出版社，1998，48～49
[35] Wylie E.B,Streeter V.L.瞬变流．北京：水利电力出版社．1983，176～186
[36] 郑文波，编著．控制网络技术．北京：清华大学出版社．2001，1～34
[37] 徐皑东，王宏，杨志家．基于以太网的工业控制网络[J]．信息与控制，2000，29(2)：61～72
[38] Kris Jams等．Internet编程．北京：电子工业出版社，1996，1～78
[39] 郑文波．控制网络与信息网络的集成．计算机世界，1999，3 12～14
[40] 陈积明等．工业以太网的研究现状及展望[J]．化工自动化及仪表，2001，28(6)：1～4
[41] Zhang W. Stability of network control systems. IEEE, Control system Magazine, 2001,84～99
[42] G C Walsh, H Ye. Scheduling of network control system. IEEE Control System Magazine, 2001,21(1): 57～65
[43] Z Wei, S. Michael, and M P Stephen. Stability of networked system. IEEE Control Magazine, 2001,21(1): 84～89
[44] L W Liou and A Ray, On modeling of integrated communication and control systems. IEEE Trans of the ASME, 1900, 113(6): 112(6)
[45] G C Walsh, H Ye, and L Bushnell, Stability analysis of networked control system, in Proc. Amer. Control Conf. San Diego, CA, 1999:2876～2880
[46] 谢勇等编．Visual C++6.0实例精通．北京：科学出版社，2000，503～535
[2] FurnessR.A. Development in Pipeline Instrumentation. Measurement and Control,1987,20(1): 7～15
[3] 陈华波，涂亚庆．输油管道泄漏检测方法综述．管道技术与设备，2000，(1)：38～41
[4] 夏海波，张来斌，王朝辉．国内外油气管道泄漏检测技术的发展现状．油气储运，2001 20(1)：1～5
[5] M.S.Yoon et al. Leak Detection Performance Specification. Pipeline Engineering. ASME 1991,34:21～26
[6] [日]森村正直，正崎弘郎主编．黄香泉译．传感器技术．北京：科学出版社，1988，405
[7] M.Nagat et al. Sensitive Liquid Sensor for Long Distance Leak Detection. SPIE,1367
[8] ELLul I.R. Advances in pipeline Leak Detection Techniques. Pipes and Pipelines International, 1989,34(3): 7～12
[9] Andy Wike. SCADA-Based Leak Detection System. Pipeline and Gas Joural, 1986, 213(6): 20
[10] 冉启文．小波分析方法及应用．哈尔滨：哈尔滨工业大学出版社，1995，5～12
[11] 刘贵忠，邸双亮．小波分析及应用．西安：西安电子科技大学，1993，6～34
[12] 秦前清，杨宗凯．实用小波分析．西安：西安电子科技大学出版社，1994，23～56
[13] 程正兴．小波分析算法与应用．西安：西安交通大学出版社，1998，32-65
[14] 杨福生．小波分析的工程分析与应用．北京：科学出版社，1999，24～35
[15] 李爱萍，段利国．小波分析在信号将消躁处理中的应用．太原理工大学学报，2001．Vol．32 No．1
[16] 李传庆，徐敏，张曙．基于小波分析的信号消躁法．应用科技，2003．Vol．30 No．2
[17] DAUBECHIES I。The wavelet transform, time-frequency localization and signal analysis [J]。 IEEE Trans, 1990,36 (5): 961～1005
[18] 何风华．小波分析在信号消躁中应用．自动检测技术，2002，Vol．21，No．6
[19] 胡昌，张君波，夏军，等．基于MATLAB的系统分析与设计——小波分析．西安：西安电子科技大学出版社，2000，223～246
[20] Wasserman P D. Radial Basis-Function Networks. in Advanced Methods in Neural Computing, Van Nostrand Reinhold, New York, U. S. A 1993,147～176
[21] Girosi F, Poggio T. Networks and the Best Approximation Property. Neuro-Control Systems:Theory and Applications, Eds. By Gupta M M and Rao D H, IEEE Press, 1994,257～264
[22] Poggio T, Girosi F. Networks for Approximation and Learning, Proc. Of IEEE, 1990, 78(9):1481～1497
[23] Leonard, J. A., Kramer, M. A. and Ungar, L. H. ,Using Radial Basis Functions to Approximate A Function and Its Error Bounds, IEEE Trans. On NNs, 1992,3(4):624～257
[24] Park J, Sandberg I W. Universal Approximation Using Radial Basis Function Network, Neural Computation, 1991,3:246～257
[25] 王洪元，史国栋．人工神经网络技术及其应用．北京：中国石化出版社，2003，61～69
[26] Narayan S Shankar,Peterson Allen M, Narasimha Medihally J. Transform domain LMS algorithm[J]. IEEE Trans Acoustics,Speech and Signal Processing, 1983,31(3): 609～615
[27] Mallat S, Hwang W L. Singularity detection and processing with wavelet. IEEE Trans. On Information Theory, 1992,38 (2): 617～643
[28] 周东华，叶银忠．现代故障诊断于容错控制．北京：清华大学出版社，2000 132～135
[29] 叶昊，王桂增，方崇智等．小波变换在故障诊断中的应用．自动化学报，1997，23(6)：736～740
[30] 喻文焕，金振东，刘峡等．信号奇异性的检测及应用．系统工程理论以应用，2000，3：125～129
[31] 陶洛文，方崇智等．以辨识为基础的长输管线故障定位．清华大学学报，1986，26(2)：69～75
[32] 王海生，叶昊，王桂增．基于小波分析的输油管道泄漏检测．信息与控制，2002．Vol．31，No．5
[33] 王立宁，靳世久等．热输原油管道瞬态压力波法泄漏点定位研究．石油学报，2000 21(4)：93～96
[34] 董绪荣，张守信等．GPS／INS组合导航定位及其应用．国防科技大学出版社，1998，48～49
[35] Wylie E.B,Streeter V.L.瞬变流．北京：水利电力出版社．1983，176～186
[36] 郑文波，编著．控制网络技术．北京：清华大学出版社．2001，1～34
[37] 徐皑东，王宏，杨志家．基于以太网的工业控制网络[J]．信息与控制，2000，29(2)：61～72
[38] Kris Jams等．Internet编程．北京：电子工业出版社，1996，1～78
[39] 郑文波．控制网络与信息网络的集成．计算机世界，1999，3 12～14
[40] 陈积明等．工业以太网的研究现状及展望[J]．化工自动化及仪表，2001，28(6)：1～4
[41] Zhang W. Stability of network control systems. IEEE, Control system Magazine, 2001,84～99
[42] G C Walsh, H Ye. Scheduling of network control system. IEEE Control System Magazine, 2001,21(1): 57～65
[43] Z Wei, S. Michael, and M P Stephen. Stability of networked system. IEEE Control Magazine, 2001,21(1): 84～89
[44] L W Liou and A Ray, On modeling of integrated communication and control systems. IEEE Trans of the ASME, 1900, 113(6): 112(6)
[45] G C Walsh, H Ye, and L Bushnell, Stability analysis of networked control system, in Proc. Amer. Control Conf. San Diego, CA, 1999:2876～2880
[46] 谢勇等编．Visual C++6.0实例精通．北京：科学出版社，2000，503～535