Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2005 | 15 | 4 | 541-550

Tytuł artykułu

Fast leak detection and location of gas pipelines based on an adaptive particle filter

Treść / Zawartość

Warianty tytułu

Języki publikacji



Leak detection and location play an important role in the management of a pipeline system. Some model-based methods, such as those based on the extended Kalman filter (EKF) or based on the strong tracking filter (STF), have been presented to solve this problem. But these methods need the nonlinear pipeline model to be linearized. Unfortunately, linearized transformations are only reliable if error propagation can be well approximated by a linear function, and this condition does not hold for a gas pipeline model. This will deteriorate the speed and accuracy of the detection and location. Particle filters are sequential Monte Carlo methods based on point mass (or ``particle'') representations of probability densities, which can be applied to estimate states in nonlinear and non-Gaussian systems without linearization. Parameter estimation methods are widely used in fault detection and diagnosis (FDD), and have been applied to pipeline leak detection and location. However, the standard particle filter algorithm is not applicable to time-varying parameter estimation. To solve this problem, artificial noise has to be added to the parameters, but its variance is difficult to determine. In this paper, we propose an adaptive particle filter algorithm, in which the variance of the artificial noise can be adjusted adaptively. This method is applied to leak detection and location of gas pipelines. Simulation results show that fast and accurate leak detection and location can be achieved using this improved particle filter.








Opis fizyczny




  • Department of Automation, Tsinghua University, Beijing 100084, China
  • Zhen Jiang Watercraft College, Zhenjiang 212003, China
  • Department of Automation, Tsinghua University, Beijing 100084, China


  • Afzal M. and Udpa S. (2002): Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline. - NDTE International, Vol. 35, No. 7, pp. 449-457.
  • Benkherouf A. and Allidina A.Y. (1988): Leak detection and location in gas pipelines. - IEE Proc., Vol. 135, Pt. D, No. 2, pp. 142-148.
  • Billmann L. (1982): Studies on improved leak detection methods for gas pipelines. - Internal Report, Institut fur Regelungstechnik, TH-Darmstadt (in German).
  • Billmann L. and Isermann R. (1987): Leak detection methods for pipelines. - Automatica, Vol. 23, No. 3, pp. 381-385.
  • Bolviken E., Acklam P.J., Christiphersen N. and Stordal J.-M. (2001): Monte-Carlo filters for non-linear state estimation. - Automatica, Vol. 37, No. 2, pp. 177-183.
  • Brodetsky E. and Savic M. (1993): Leak monitoring system for gas pipelines. - IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Minneapolis, USA, Vol. 3, pp. 17-20.
  • Chernick M.R. and Wincelberg D.M. (1985): Time series analysis of the strategic petroleum reserve's brine pipeline test data. - Comput. Math. Applic., Vol. 11, No. 9, pp. 967-982.
  • Dinis J.M., Wojtanowicz A.K. and Scott S.L. (1999): Leak detection in liquid subsea flowlines with no recorded feed rate. - J. Energy Res. Tech., Vol. 121, No. 3, pp. 161-166.
  • Doucet A., Godsill S. and Ardrieu C. (2000): On sequential Monte-Carlo sampling methods for Bayesian filtering. - Statist. Comput., Vol. 10, No. 3, pp. 197-208.
  • Ellul I.R. (1989): Advances in pipeline leak detection techniques. - Pipes and Pipelines Internat., Vol. 34, No. 3, pp. 7-12.
  • Fukuda T. and Mitsuoka T. (1983): Leak detection and localization in a pipeline system based on time series analysis techniques. - J. Fluid Contr., Vol. 15, No. 4, pp. 5-17.
  • Furness R.A. (1985): Modern pipeline monitoring techniques. - Pipes and Pipelines Internat., Vol. 30, No. 3, pp. 7-11.
  • Gertler J. (1998): Fault Detection and Diagnosis in Engineering Systems. - New York: Marcel Dekker.
  • Gordon N.J., Salmond D.J. and Smith A.F.M. (1993): Novel approach to nonlinearnon-Gaussian Bayesian state estimation. - IEE Proc.-F, Vol. 140, No. 2, pp. 107-113.
  • Hough J.E. (1988): Leak testing of pipelines uses pressure and acoustic velocity. - Oil and Gas J., Vol. 86, No. 47, pp. 35-41.
  • Ikuta K., Yoshikane N., Vasa N., Oki Y., Maeda M., Uchiumi M., Tsumur Y., Nakagawa J. and Kawada N. (1999): Differential absorption lidar at 1.67 μm for remote sensing of methane leakage. - Jpn. J. Phys., Vol. 38, No. 1A, pp. 110-114.
  • Iseki T., Tai H. and Kimura K. (2000): A portable remote methane sensor using a tunable diode laser. - Meas. Sci. Technol., Vol. 11, No. 6, pp. 594-602.
  • Kadirkamanathan V., Li P., Jaward M.H. and Fabri S.G. (2000): A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems. - Proc. IEEE Conf. Decision Contr., Sydney, Australia, pp. 4341-4346.
  • Kitagawa G. (1996): Monte-Carlo filter and smoother for non-Gaussian nonlinear state space models. -J. Comput. Graph. Statist., Vol. 5, No. 1, pp. 1-25.
  • Klein W.R. (1993): Acoustic leak detection. - Amer. Soc.Mech. Eng., Petroleum Division (Publication) PD, Vol. 55, pp. 57-61.
  • Korbicz J., Koscielny J.M., Kowalczuk Z. and Cholewa W. (Eds.) (2004): Fault Diagnosis. Models, Artificial Intelligence, Applications. - Berlin: Springer.
  • Kowalczuk Z. and Gunawickrama K. (2004): Detecting and locating leaks in transmission pipelines, In: Fault Diagnosis. Models, Artificial, Intelligence, Applications (J. Korbicz, J.M. Koscielny, Z. Kowalczuk and W. Cholewa, Eds.). - Berlin: Springer.
  • Li P. and Kadirkamanathan V. (2001): Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems. - IEEE Trans. Syst., Man Cybern. - Part C: Applic. Rev., Vol. 31, No. 3, pp. 337-343.
  • Muhlbauer W.K. (2004): Pipeline Risk Management Manual. - Burlington: Gulf Professional Publishing.
  • Patton R.J., Frank P.M. and Clark R.N. (Eds.) (2000): Issues of Fault Diagnosis for Dynamic Systems. - Berlin: Springer.
  • Reichardt T.A., Einfield W. and Kulp T.J. (1999): Review of remote detection for natural gas transmission pipeline leaks. - Technical Report, NETL, Sandia National Laboratories, Albuquerque, NM.
  • Shields D.N., Ashton S.A. and Daley S. (2001): Design of nonlinear observers for detecting faults in hydraulic sub-sea pipelines. - Contr. Eng. Pract., Vol. 9, No. 3, pp. 297-311.
  • Sivathanu Y. (2003): Natural gas leak detection in pipelines. - Technology Status Report, En'Urga Inc., West Lafayette, IN.
  • Spaeth L. and O'Brien M. (2003): An additional tool for integrity monitoring. - Pipeline and Gas J., Vol. 230, No. 3, pp. 41-43.
  • Sperl J.L. (1991): System pinpoints leaks on Point Arguello offshore line. - Oil and Gas J., Vol. 89, No. 36, pp. 47-52.
  • Tiang X. (1997): Non-isothermal long pipeline leak detection and location. - Atca Scientiarum Naturalium Universitis Pekinensis, Vol. 33, No. 5, pp. 574-580.
  • Tracer Research Corporation (2003): Patent product. -Available at
  • Verde C. (2005): Accommodation of multi-leak location in a pipeline. - Contr. Eng. Pract., Vol. 13, No. 8, pp. 1071-1078.
  • Wang G., Dong D. and Fang C. (1993): Leak detection for transport pipelines based on autoregressive modeling. - IEEE Trans. Instrum. Meas., Vol. 42, No. 1, pp. 68-71.
  • Wylie E.B. and Streeter V.L. (1993): Fluid Transients in Systems. - Englewood Cliffs: Prentice-Hall.
  • Zhou D.H., Xi Y.G. and Zhang Z.J. (1991): Nonlinear adaptive fault detection filter. - Int. J. Syst. Sci., Vol. 22, No. 12, pp. 2563-2571.
  • Zhou D.H. and Frank P.M. (1996): Strong tracking filtering of nonlinear time-varying stochastic systems with colored noise with application to parameter estimation and empirical robustness analysis. - Int. J. Contr., Vol. 65, No. 2, pp. 295-307.
  • Zhao Q. and Zhou D.H. (2001): Leak detection and location of gas pipelines based on a strong tracking filter. - Trans. Contr. Automat. Syst. Eng., Vol. 3, No. 2, pp. 89-94.

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.