A Large Dimensional Data-driven Fuzzy Detection Method for Oil-gas Pipeline Network Leakage
-
摘要: 输油管网状态量多及工艺复杂,难以建立精确的管网数学模型,为了能够实时监控管网的安全运行情况,本文提出一种基于大维数据驱动的管网泄漏监控模糊决策方法.首先利用管网现有的数据信息,在不对数据进行降维处理的情况下,从信息物理系统的角度出发,将油气管网的拓扑结构、阀门开度等管道物理数据以及压力、流量等运行信息数据结合起来对复杂管网系统建立数据驱动模型.然后基于大维随机矩阵谱理论,将得到的信息物理数据协方差矩阵谱分布及圆环率作为模糊决策的条件对管网运行情况进行判断.当管网拓扑发生动态变化时,提出的方法可以有效地解决误报率高的问题.最后通过仿真及实例的分析,可以证明所提出方法的有效性.Abstract: It is difficult to build an exact math model for the oil-gas pipeline network because there are multiple state variables and complex processing technology. A leakage detection method using fuzzy detection based on large dimensional data for oil pipeline network is proposed, which can monitor oil pipeline network operational status in real time. Firstly, from the view-point of cyber-physical system (CPS), existing oil pipeline network data containing network topology, physical data like valve opening, and operating data like pressure and flow are used to build a data-driven model without dimensionality reduction. Then, as for fuzzy decision conditions, the eigenvalue spectrum distribution and ring law based on spectral analysis of large dimensional random matrices are used to judge the network operating status. When the topological structure of oil pipeline network is dynamically altered, the fuzzy method can solve the problem of high rate false alarm. Finally, the effectiveness of the proposed method is demonstrated through simulation and case study.1) 本文责任编委 文成林
-
表 1 不同维度对比结果
Table 1 The results of different dimension ratio
维度比 正常情况特征值均值MSR_N 异常情况特征值均值MSR_A 均值差比值 0.1 0.97 0.83 0.144 0.3 0.92 0.78 0.152 0.5 0.88 0.71 0.193 0.7 0.84 0.69 1.179 0.9 0.77 0.64 1.169 -
[1] 王桂增, 叶昊.流体输送管道的泄漏检测与定位.北京:清华大学出版社, 2010.Wang Gui-Zeng, Ye Hao. Leak Detection and Positioning in Fluid Transmit Pipeline. Beijing:Tsinghua University Press, 2010. [2] Zhang Y, Chen S L, Li J, Jin S J. Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter. Measurement, 2014, 49:382-389 doi: 10.1016/j.measurement.2013.12.009 [3] Feng J, Zhang H G. Diagnosis and localization of pipeline leak based on fuzzy decision-making method. Acta Automatica Sinica, 2005, 31(3):484-490 [4] 刘金海, 冯健.基于模糊分类的流体管道泄漏故障智能检测方法研究.仪器仪表学报, 2011, 32(1):26-32 http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201101006.htmLiu Jin-Hai, Feng Jian. Research on leak fault intelligent detection method for fluid pipeline based on fuzzy classification. Chinese Journal of Scientific Instrument, 2011, 32(1):26-32 http://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201101006.htm [5] Huang Y C, Lin C C, Yeh H D. An optimization approach to leak detection in pipe networks using simulated annealing. Water Resources Management, 2015, 29(11):4185-4201 doi: 10.1007/s11269-015-1053-4 [6] Ozevin D, Harding J. Novel leak localization in pressurized pipeline networks using acoustic emission and geometric connectivity. International Journal of Pressure Vessels and Piping, 2012, 92:63-69 doi: 10.1016/j.ijpvp.2012.01.001 [7] Kim Y, Lee S J, Park T, Lee G, Suh J C, Lee J M. Robust leak detection and its localization using interval estimation for water distribution network. Computers and Chemical Engineering, 2016, 92:1-17 doi: 10.1016/j.compchemeng.2016.04.027 [8] 侯忠生, 许建新.数据驱动控制理论及方法的回顾和展望.自动化学报, 2009, 35(6):650-667 http://www.aas.net.cn/CN/abstract/abstract13327.shtmlHou Zhong-Sheng, Xu Jian-Xin. On data-driven control theory:the state of the art and perspective. Acta Automatica Sinica, 2009, 35(6):650-667 http://www.aas.net.cn/CN/abstract/abstract13327.shtml [9] 王宏, 柴天佑, 丁进良, 布朗马丁.数据驱动的故障诊断与容错控制:进展与可能的新方向.自动化学报, 2009, 35(6):739-747 http://www.aas.net.cn/CN/abstract/abstract13334.shtmlWang Hong, Chai Tian-You, Ding Jin-Liang, Brown M. Data driven fault diagnosis and fault tolerant control:some advances and possible new directions. Acta Automatica Sinica, 2009, 35(6):739-747 http://www.aas.net.cn/CN/abstract/abstract13334.shtml [10] 代伟, 柴天佑.数据驱动的复杂磨矿过程运行优化控制方法.自动化学报, 2014, 40(9):2005-2014 http://www.aas.net.cn/CN/abstract/abstract18472.shtmlDai Wei, Chai Tian-You. Data-driven optimal operational control of complex grinding processes. Acta Automatica Sinica, 2014, 40(9):2005-2014 http://www.aas.net.cn/CN/abstract/abstract18472.shtml [11] 王中杰, 谢璐璐.信息物理融合系统研究综述.自动化学报, 2011, 37(10):1157-1166 http://www.aas.net.cn/CN/abstract/abstract17604.shtmlWang Zhong-Jie, Xie Lu-Lu. Cyber-physical systems:a survey. Acta Automatica Sinica, 2011, 37(10):1157-1166 http://www.aas.net.cn/CN/abstract/abstract17604.shtml [12] 温景容, 武穆清, 宿景芳.信息物理融合系统.自动化学报, 2012, 38(4):507-517 http://www.aas.net.cn/CN/abstract/abstract17704.shtmlWen Jing-Rong, Wu Mu-Qing, Su Jing-Fang. Cyber-physical system. Acta Automatica Sinica, 2012, 38(4):507-517 http://www.aas.net.cn/CN/abstract/abstract17704.shtml [13] Khaitan S K, McCalley J D. Design techniques and applications of cyberphysical systems:a survey. IEEE Systems Journal, 2015, 9(2):350-365 doi: 10.1109/JSYST.2014.2322503 [14] Bai Z D, Silverstein J W. Spectral Analysis of Large Dimensional Random Matrices (Second edition). New York:Springer-Verlag, 2010. [15] 刘强, 秦泗钊.过程工业大数据建模研究展望.自动化学报, 2016, 42(2):161-171 http://www.aas.net.cn/CN/abstract/abstract18807.shtmlLiu Qiang, Qin S J. Perspectives on big data modeling of process industries. Acta Automatica Sinica, 2016, 42(2):161-171 http://www.aas.net.cn/CN/abstract/abstract18807.shtml [16] Stubbs S, Zhang J, Morris J. Fault detection in dynamic processes using a simplified monitoring-specific CVA state space modelling approach. Computers and Chemical Engineering, 2012, 41:77-87 doi: 10.1016/j.compchemeng.2012.02.009 [17] 樊继聪, 王友清, 秦泗钊.联合指标独立成分分析在多变量过程故障诊断中的应用.自动化学报, 2013, 39(5):494-501 http://www.aas.net.cn/CN/abstract/abstract17927.shtmlFan Ji-Cong, Wang You-Qing, Qin S J. Combined indices for ICA and their applications to multivariate process fault diagnosis. Acta Automatica Sinica, 2013, 39(5):494-501 http://www.aas.net.cn/CN/abstract/abstract17927.shtml [18] 颜雪军, 赵春霞, 袁夏. 2DPCA-SIFT:一种有效的局部特征描述方法.自动化学报, 2014, 40(4):675-682 http://www.aas.net.cn/CN/abstract/abstract18333.shtmlYan Xue-Jun, Zhao Chun-Xia, Yuan Xia. 2DPCA-SIFT:an efficient local feature descriptor. Acta Automatica Sinica, 2014, 40(4):675-682 http://www.aas.net.cn/CN/abstract/abstract18333.shtml [19] 张化光, 何希勤.模糊自适应控制理论及其应用.北京:北京航空航天大学出版社, 2002.Zhang Hua-Guang, He Xi-Qin. Theory and Application of Fuzzy Self adaptive Control. Beijing:Beijing University of Aeronautics and Astronautics Press, 2002. [20] Zhang H G, Zhang J L, Yang G H, Luo Y H. Leader-based optimal coordination control for the consensus problem of multiagent differential games via fuzzy adaptive dynamic programming. IEEE Transactions on Fuzzy Systems, 2015, 23(1):152-163 doi: 10.1109/TFUZZ.2014.2310238 [21] Yang F S, Zhang H G, Wang Y C. An enhanced input-delay approach to sampled-data stabilization of T-S fuzzy systems via mixed convex combination. Nonlinear Dynamics, 2014, 75(3):101-512 [22] 杨贵军, 蒋朝辉, 桂卫华, 杨春华, 谢永芳.基于熵权-可拓理论的高炉软熔带位置状态模糊综合评判方法.自动化学报, 2015, 41(1):75-83 http://www.aas.net.cn/CN/abstract/abstract18585.shtmlYang Gui-Jun, Jiang Zhao-Hui, Gui Wei-Hua, Yang Chun-Hua, Xie Yong-Fang. Fuzzy synthesis evaluation method for position state of blast furnace cohesive zone based on entropy weight extension theory. Acta Automatica Sinica, 2015, 41(1):75-83 http://www.aas.net.cn/CN/abstract/abstract18585.shtml [23] Guionnet A, Krishnapur M, Zeitouni O. The single ring theorem. Annals of mathematics, 2011, 174(2):1189-1217 doi: 10.4007/annals.2011.174-2 [24] Benaych-Georges F, Rochet J. Outliers in the single ring theorem. Probability Theory and Related Fields, 2016, 165(1-2):313-363 doi: 10.1007/s00440-015-0632-x