Leak Monitoring in Pipeline Networks Using Wavelet Analysis

Leaks and blockages in fluid filled pipeline networks are one of t he major concerns in chemical, gas, oil and water distribution industries. Current monitori g procedures of these networks require the use of multiple sensors distributed throughout the pipes. These include: pressure sensors, flow meters and/or valve sensors. Leak detection is usually accomplished by monitoring the fluid flow rate in and out of a pipe. Recent work in this area has shown applications of wave reflections identified from the water hammer tests. Thi s paper proposes a wavelet-based procedure for leak severity monitoring. The method detects singula rities in reflected pressure waves. The method is validated through pipe network simulations. Pressure waves are simulated using time domain based transmission line modelling techniques. The st udy shows great potential for this method of leak severity identification which can be applied to many industria l applications. Introduction A number of pipeline network monitoring methods and leak detection techniques have been developed over the last forty years. These developments are prim arily driven by environmental and financial factors. Severe leaks can lead to water shortages or nvironmental catastrophes. Water leaks are associated usually with ageing infrastructure of wa ter distribution systems. Environmental leaks are one of the major concerns in chemical and oil industries. The need for rel atively cheap and simple methods to monitor pipelines is of great interest to all f luid pipeline network owners and users. One of the major disadvantages of current leak detection met hods is the need for the use of multiple sensors. Traditionally, pipeline networks are monitored using var ious types of flow meters, pressure and valve sensors. Flow meters are used to monitor the f low rate at various points of the network. As reported in [1], leak monitoring techniques include acoustic methods, tracer injection and video inspection,. Various methods of signal processing are used to as sis the analysis of sensor data. These include: sensitivity studies [2] and non-linear state observers [3]. Recent developments in the area of leak detection have shown the poss ibilitie of leak detection using wave reflections from water hammer tests [4,5]. The basic idea of this method is to use water hammer to create waves, which propagate through the system. Refl ection waves are captured by a single pressure transducer. Additional reflection points can be rela t d to various features, including potential leaks. More recently, a reliable wavelet-based method for the identification of reflection points in pipeline networks has been proposed [6]. The method has been validated to detect various pipeline networks leaks. The aim of this paper is to extend the study of pipeline network lea k d tection to the identification of the severity of leaks. The paper starts with a brief introducti on to pipeline network modelling. Then a short section describing the wavelet method used for singulari ty detection is then included. Finally, the analysis of simulated results focusing on leak sev erity (simulated by a change in pressure resistance) forms the next step in showing the utility of this metho d. Key Engineering Materials Online: 2003-07-15 ISSN: 1662-9795, Vols. 245-246, pp 51-58 d i:10.4028/www.scientific. net/KEM.245-246.51 © 2003 Trans Tech Publications Ltd, Switzerland All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications Ltd, www.scientific. net. (Semanticscholar.org-13/03/20,20:46:12) Reflection Waves in Pipes Water hammer, which creates pressure waves to propagate in pipes can be used for leak monitoring. When a pressure wave is introduced to a ridgid pipe, it travels to t he ther end of the pipe. The wave is delayed and the amplitude of the wave is attenuated, m ainly due to fluid friction. The attenuation can be equated to the steady state pressure loss. Al so, as waves bounce around a pipe network system, they encounter features such as changes in sect ion, resistances( such as valves), and junctions of three or more pipes. As a result the incident waves exhibits three major wave phenomena: • reflection some energy of the wave is reflected back; • absorption the amplitude of the wave is attenuated; • transmission the wave continues down the other pipes in the junction. It is clear that the intensity of these effects depends on t he admittance Y (reciprocal of resistance) at the junction of each of the pipes and the geometry of the feature. T he entire process can be modelled using various simulation approaches. These include: the method of c haracteristics [7,8], bond graph modelling [9], modal analysis [10] and the transmission modeling t chnique [11,12]. Although, the first method is the most commonly applied techniques used to model waves in pipeline networks, the simplified transmission technique is efficient and sufficiently accurate to model fluid transmission networks. Transmission line modelling is in fact a method of characteristics modellin g which utilises a straight (linear) characteristic line whose properties do not vary with the operating condition. A description of the transmission line modelling software, the physics and mat he atics waves in pipline networks can be found in [13]. Orthogonal Wavelet Transform Wavelet analysis is a linear transformation that uses a basis of pecial compressed and stretched functions, called wavelets, for signal decomposition. Wavelet functions ha ve a number of useful properties suitable for the analysis of pressure waves in pipe net works. Leaks and blockages in fluid filled pipeline networks exhibit pressure drops and reflections. These f atures are in fact discontinuities or singularities in pressure wave functions. Wavelets are well suited to detect discontinuities, abrupt changes in signals and singularities in funct ions. This problem has been widely studied in the literature. Singularities in functions are of t n detected using wavelet local maxima at fine scales, as shown in [14] The most common approach uti lises he continuous approach. The work presented in this paper utilises the discrete wa velet transform for the analysis of singularities. For the sake of completeness, a brief introduction to the wavelet anal ysis performed in the current study is given below. This includes the basic definitions. For more details the reader is referred to [14,15]. Discrete wavelets are often associated with dilation equati on and scaling functions. The basic scaling function ) t ( φ can be defined as ) k t 2 ( c ) t ( 1 N