Long range pipeline leak detection and localization using discrete observer and support vector machine

被引:34
作者
Xie, Junyao [1 ]
Xu, Xiaodong [1 ]
Dubljevic, Stevan [1 ]
机构
[1] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada
关键词
leak detection and localization; discrete Luenberger observer; Cayley-Tustin discretization; distributed parameter systems; long pipeline; support vector machine; MODEL; SVM;
D O I
10.1002/aic.16532
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
A realistic pipeline modeled by a nonlinear coupled first-order hyperbolic partial differential equations (PDEs) system is studied for the long transportation pipeline leak detection and localization. Based on the so-called water hammer equation, a linear distributed parameter system is obtained by linearization. The structure and energy preserving time discretization scheme (Cayley-Tustin) is used to realize a discrete infinite-dimensional hyperbolic PDEs system without spatial approximation or model order reduction. In order to reconstruct pressure and mass flow velocity evolution with limited measurements, a discrete-time Luenberger observer is designed by solving the operator Riccati equation. Based on this distributed observer system, data on different normal and leakage conditions (various leak amounts and positions) are generated and fed to train a support vector machine model for leak detection, amount, and position estimation. Finally, the leak detection, amount estimation, and localization effectiveness of the developed method are proved by a set of simulations. (c) 2019 American Institute of Chemical Engineers AIChE J, 65: e16532 2019
引用
收藏
页数:20
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