A SVM-based pipeline leakage detection and pre-warning system

被引:147
作者
Qu, Zhigang [1 ]
Feng, Hao [1 ]
Zeng, Zhoumo [1 ]
Zhuge, Jingchang [1 ]
Jin, Shijiu [1 ]
机构
[1] Tianjin Univ, State Key Lab Precis Measuring Technol & Instrume, Tianjin 300072, Peoples R China
基金
中国国家自然科学基金;
关键词
Pipeline leakage; Pre-warning; Distributed optical fiber; Support vector machine; Wavelet packet;
D O I
10.1016/j.measurement.2009.12.022
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A SVM-based pipeline leakage detection and pre-warning system is presented in this paper. In the system an optical cable is laid in parallel with a pipeline in the same ditch and three single mode optical fibers inside constitute the distributed vibration sensor. The sensor is based on Mach-Zehnder optical fiber interferometer and can detect the vibration signals along a pipeline in real time. Then the eigenvectors of vibration signals are extracted by "energy-pattern" method based on wavelet packet decomposition. Subsequently the vibration signals are recognized by support vector machine (SVM) through the features so that it can judge whether any abnormal event is taking place. If any abnormal event is found along a pipeline, the location is thus calculated. A series of trials in situ have been done, showing that the system is of good accuracy and real time performance both in recognition and locating. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:513 / 519
页数:7
相关论文
共 14 条
[1]  
[Anonymous], 1992, SOC IND APPL MATH
[2]   Multicategory classification by support vector machines [J].
Bredensteiner, EJ ;
Bennett, KP .
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 1999, 12 (1-3) :53-79
[3]   A tutorial on Support Vector Machines for pattern recognition [J].
Burges, CJC .
DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (02) :121-167
[4]   SUPPORT-VECTOR NETWORKS [J].
CORTES, C ;
VAPNIK, V .
MACHINE LEARNING, 1995, 20 (03) :273-297
[5]   A comparison of methods for multiclass support vector machines [J].
Hsu, CW ;
Lin, CJ .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (02) :415-425
[6]  
Li Jianmin, 2003, Journal of Tsinghua University (Science and Technology), V43, P120
[7]  
QU Z, 2006, P 6 INT PIP C, P677
[8]  
Vapnik V., 1995, The nature of statistical learning theory
[9]  
YANG G, 2001, J VIBRATION SHOCK, V20, P25
[10]  
YANG J, 2004, CONTROL INSTRUMENTS, V31, P1