Laboratory investigation of a multiple-model state estimation scheme for detection and isolation of leaks in pipelines

Abstract

A pipeline system is an essential component in the transport of natural gases, crude and oil products in the petroleum industry, as well as gas and water in vital utility systems. Early detection of leaks in pipelines is essential to avoid excessive economical loss and reducing the environmental and health hazards that normally result from undetected leaks. In this paper, a model-based estimation scheme, which was developed as a basis for real-time monitoring of fluid flow in pipelines, is tested experimentally. In this estimation scheme, the fluid flow in a pipeline, which is modelled by a set of non-linear partial differential equations, is represented in a state-space form. A modified extended Kalman filter (MEKF), with its internal model defined by the obtained state-space form, is invoked together with feedforward computations to establish an adaptive multimodel state estimation technique. A laboratory experimental test-rig is constructed to test the validity and effectiveness of the developed leak detection and localization scheme. Experimental results utilizing the laboratory set-up are presented to demonstrate that the developed scheme effectively detects and locates leaks in pipelines within a short time duration.

Keywords

pipeline fluid flow pipeline leak detection leak isolation Kalman filter

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References

Brones

H. H.

Schaffhaussen

European methods of leak detection and location

Pipeline Industry, May 1972, 50–66.

Scott

S. L.

Barrufet

M. A.

Worldwide assessment of industry leak detection capabilities for single and multiphase pipelines. TR 1435-01-99-CA-31003, Offshore Technology Research Center, University of Texas at Austin, August 2003.

De Read

J. R.

Comparison between ultrasonic and magnetic flux pigs for pipeline inspection

Pipes and Pipeline Int., January-February 1987, 7–15.

Liou

J. C.P.

Tian

Leak detection: A transient flow simulation approach

Trans. ASME, Pipeline Engng, 1994, PD-Vol. 60, 51–58.

Wilsky

A. S.

A survey of design methods for failure detection systems

Automatica, 1976, 12, 601–611.

Siebert

A simple method for detection and locating leaks in gas pipeline

Process Automn, 1981, 2, 90–95.

Isermann

Process fault detection based on modeling and estimation methods - a survey

Automatica, 1984, 20, 387–404.

Billmann

Isermann

Leak detection methods for pipelines

Automatica, 1987, 23 (3), 381–385.

Isermann

Freyermuth

Process fault diagnosis based on process model knowledge - Part I: Principles for fault diagnosis with parameter estimation

J. Dynamic Systems, Measmt, and Control, December 1991, 113, 620–626.

10.

Isermann

Process fault diagnosis with parameter estimation methods. In Seventh IFAC/IFIP Conference on Digital computer applications, Vienna, Austria, 1985, pp. 51–60.

11.

Wang

Dong

Fang

Leak detection for transport pipelines based on autoregressive modeling

IEEE Trans. Instrum. Measmt, February 1993, 42 (1), 68–71.

12.

Jones

H. L.

Failure detection in linear systems. PhD Dissertation, Department of Aeronautics and Astronautics, MIT, Cambridge, Massachusetts, September 1973.

13.

Mehra

R. K.

Peschon

An innovations approach to fault detection and diagnosis in dynamic systems

Automatica, 1971, 7, 637–640.

14.

Willsky

A. S.

Jones

H. L.

A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems

IEEE Trans. Autom. Control, 1976, AC-21, 108–112.

15.

Willsky

A. S.

Failure detection in dynamic systems

AGARD, 1980, 109.

16.

Billmann

Isermann

Leak detection methods for pipelines

Automatica, 1987, 23 (3), 381–385.

17.

Loparo

K. A.

Buchner

M. R.

Vasudeva

K. S.

Leak detection in an experimental heat exchanger process: A multiple model approach

IEEE Trans. Autom. Control, 1991, 36, 167–177.

18.

Poulakis

Valougeorgis

Papadimitriou

Leakage detection in water pipe networks using a Bayesian probabilistic framework

Probabilistic Engng Mechanics, October 2003, 18 (4), 315–327.

19.

Kingsley

Abhulimen Susu

A. A.

Liquid pipeline leak detection system: Model development and numerical simulation

Chem. Engng J., 15 January 2004, 97 (1), 47–67.

20.

Verde

Accommodation of multi-leak location in a pipeline

Control Engng Practice, August 2005, 13 (8), 1071–1078.

21.

Emara-Shabaik

H. E.

Khulief

Y. A.

Hussaini

A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation

Proc. Instn Mech. Engrs, Part I: J. Systems and Control Engineering, 2002, 216 (16), 497–512.

22.

Emara-Shabaik

H. E.

Khulief

Hussaini

Simulation of transient flow in pipelines for computer-based operations monitoring

Int. J. Numer. Meth. Fluids, 2004, 44, 257–275.

23.

Belsito

Lombardi

Andreussi

Banerjee

Leak detection in liquified gas pipelines by artificial neural networks

Am. Inst. Chem. Engrs J., 1998, 44 (2), 2675–2687.

24.

Bentley

J. P.

Principles of measurement systems, 3rd edition, 1995 (Longman, London).

25.

Caputo

A. C.

Pelagagge

P. M.

An inverse approach for piping networks monitoring

J. Loss Prevention in the Process Industries, November 2002, 15 (6), 497–505.

26.

Buzzi

Conte

De Maio

Lops

Optimum diversity detection over fading dispersive channels with non-Gaussian noise

IEEE Trans. Signal Processing, 2001, 49 (4), 767–776.