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Mechanisms for Water Pipeline Monitoring System

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The monitoring of leaks in pipelines is an important issue to be addressed by researchers and the public. This is due the fact that they can have a great impact both economically and environmentally. In recent years, the effect of leakages of pipelines carrying oil, gas and nuclear fluids have posed a threat on humans as well as marine life. This paper provides a survey of recent methods of detecting pipeline leaks with special focus on Real Time Transient Modeling and Wave Propagation Method is implemented to detect and locate the position of the leak in a water pipeline. A mathematical model is carried out to solve the transient based leak detection model and different scenarios are developed to estimate the relationship between the pressure fluctuation and leak position. The obtained results approve the potentiality of the proposed technique.


Water distribution is generally installed through underground pipes. Monitoring the underground water pipelines is more difficult than monitoring the water pipelines located on the ground in open space. This situation will cause a permanent loss if there is a disturbance in the pipeline such as leakage. Leaks in pipes can be caused by several factors, such as the pipe’s age, improper installation, and natural disasters. Therefore, a solution is required to detect and to determine the location of the damage when there is a leak. Wireless Sensor Network (WSN) is considered as a reliable solution for Pipeline Leak Detection Systems (PLDS) to supervise pipeline and to detect and localize leaks.


Combining the RTTM (Real Time Monitoring System Method) [4] and the Wave Propagation Method (WPM) for water leak monitoring and pipe modeling. The rest of paper is organized as follows: section II reviews the previous implemented hybrid pipeline leak detection methods. Section III details and describes the water pipeline model. In section IV, we detail the PLDS architecture. Section V illustrates the leak detection methodology. Finally, section VII concludes this paper. we focus on sensing the continuously water parameters (pressure and flow rate) to detect the presence of the leak and to locate its position. Thus, the originality of our contribution is to deploy a hybrid method.

A. Real time transient modelling

Verde and Visairo (2001) proposed a method, which uses a linearized, discretized pipe flow model on an N-node grid and a bank of observers. The observers are modeled in such a way that when leakage occurs, all observers are reset except one. Localization of the leakage is obtained by the location of the non-responsive observer. Meanwhile, the quantity the leak can be obtained from the output of the other observers. Moreover, a detection system that utilizes an adaptive Luenberger-Type observer, based on a set of two-coupled one dimensional first order nonlinear hyperbolic partial differential equation, is proposed by (Aamo et al. 2006; Hauge et al. 2007). Although this method is able to detect tiny leaks [less than 1 % of flow (Scott and Barrufet 2003)], it has the drawback of having high cost, as it requires huge instrumentation for obtaining data in real time. Moreover, another disadvantage of this method is the complexity of models employed that can be handled only by an expert.

This method depends on pipe flow models developed to employing equations such as: conservation of momentum, mass and energy as well as the equation of state of the fluid. The presence of leakage is determined by the estimated value and measured value of the flow. Continuous monitoring noise levels and transient events minimize false alarm rate. Billmann and Isermann (1987) designed an observer with friction adaptation that in the event of leakage it generates a different output from one obtained from measurements. Thus, from this difference leakage can be detected.

B. Negative pressure wave method

In the negative pressure wave method, once a leak occurs the pressure of the fluid drops. This is due to the sudden decrease of liquid density at the position of the leak. Subsequently, pressure wave source propagates outwards for the point of leakage towards the opposite sides of the leak. Considering the pressure of the fluid before and after the leak as a reference, the wave produced by such leakage is termed the negative pressure wave.

As this negative pressure wave travels towards the terminal ends of the pipeline section, pressure sensors stationed at the terminal ends are able to measure the pressure reduction signal. This can be achieved because when the wave reaches the terminal ends, it causes a drop first at the station inlet pressure and then the station outlet pressure. Since the leakage can be at any random point on the pipeline section, different time difference of the negative pressure wave is obtained at the terminal ends. From the knowledge of the different time difference that the pressure sensors on both sides of the leak detect, the pipeline section length and negative pressure wave velocity, the position of the leak can be obtained (Ge et al.2008; Ma et al. 2010).

C. Digital signal processing

Digital signal processing is one of the alternative methods for leak detection (USDT 2007). In the set-up stage, the output obtained from the system due to a known alteration in flow is obtained. Subsequently, digital signal processing is carried on the obtained measurements in order to detect variations in system response. The application of digital signal processing helps in isolation of original leak responses from noisy data. Encouraging results have been obtained from the application of this method for both gas and liquid pipelines (Golby and Woodward 1999; USDT 2007). The main advantage of this method is that the mathematical model of the pipeline is not needed. However, just like the statistical method, if there is a leak in the set-up phase, it will not be detected until its size grows substantially. An additional disadvantage of this method is its high cost and complexity when it comes to installation and testing.

D. Mass balance method

The mass balance method for leak detection is straightforward (Burgmayer and Durham 2000; Martins and Seleghim 2010). It is based on the principle of mass conservation. The existence of leak causes an imbalance between the output and input mass flow rate as well as the line pack variable rate (Liou 1996; Parry et al. 1992). This is variable that defines the actual amount of gas in a pipeline or distribution system. A leak alarm is raised once the difference between the volume of fluid entering a section of the pipeline and the volume of the fluid leaving the section exceeds some pre-set threshold. (Liu 2008) presented a detailed theory and the implementation issues that are encountered in this method. In their work, they further pointed out that the volume or mass can be obtained by using readings of commonly used process variables such as temperature, pressure and flow rate. (Rougier 2005) presented a hybrid mass balance method, which incorporates probabilistic method to the mass balance method. The main drawback of this method is that the probabilistic method requires a substantial amount of computational power. One of the advantages of the mass balance method however is the ease with which it can be implemented on existing pipeline infrastructure. It is also able to rely on existing instrumentation already available on the pipeline; thus, resulting in low cost implementation (Murvay and Silea 2012; Wan et al. 2011). However, its performance relies on the size of the leak, frequency at which balance measurements are obtained as well as on the overall accuracy of measuring instruments. Another limitation of the mass balance method is its inability to detect small leak in real-time. Thus, resulting in loss of significant amount of fluid before an alarm is raised. A further limitation is that the mass balance method easily affected by random disturbances around the pipeline as well as the pipe dynamics.

Thus, unless the threshold values are adapted, high false alarm rates will be recorded during transient periods of the pipeline. Moreover, unless a localization technique is attached to the method, it cannot localize the actual location of the leak on its own.


Global architecture

The global architecture is divided into two subsystems: WSN system and Remote Control Centre (RCC). For each segment i of the pipeline, WSNi system is Responsible for collecting monitored water pressure and flow rate parameters by the use of autonomous sensors. Firstly, the segment i of pipeline is divided into equal segments and sensor nodes are placed in each segment ends. Then, hierarchical WSN architecture is implemented where sensors are grouped into clusters. Each cluster head transmits the data to a Base Station (BSi) which will be analysed by the RCC to recognize the presence of the leak and its position. Hybrid method is implemented as following:

  • Leak location: Once the leak is identified, the WPM is employed to locate the leak point..
  • Leak Detection: RTTM method: The pressure-flow profile of the pipeline is calculated based on the measurements of the pipeline inlet and outlet. Substituting the collected measurements into a mathematical model, the predicted operating parameters can be evaluated by employing the Method of Characteristics (MOC). Preliminary leak detection is considered by comparing the predicted modelled values to the measured values.

B. Leak Localization approach

The pressure near the valve undergoes a pressure surge (∆P1) as the dynamic pressure of the fluid converts to hydrostatic pressure. A positive pressure wave is generated, and travels upstream along the pipeline. Arriving at the leak point, a sudden drop by ∂ value occurs in the pressure. A negative pressure wave is produced and starts to propagate downstream. A Pressure Recorder (PR) collects the pressure data.


In this paper, in order to guarantee a suitable water pipeline monitoring in this project, we have implemented hybrid technique that combines the RTTM method for real-time leak detection with the wave propagation method for leak localization. To evaluate the localization method, a location error is calculated to test the localization accuracy which depends on the distance from the pipeline inlet. The obtained results are acceptable. However, in the next work, we will enhance the location accuracy by combining the implemented localization method with an intelligent algorithm allowing to reinforce its results and to be certain about the leak position.


[1] P.-S. Murvay and I. Silea, “A survey on gas leak detection and localization techniques,” Journal of Loss Prevention in the Process Industries, vol. 25, no. 6, pp. 966–973, 2012

[2] T. El-Shiekh, “Leak detection methods in transmission pipelines,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 32, no. 8, pp. 715–726, 2010

[3] T. Zhang, Y. Tan, X. Zhang, and J. Zhao, “A novel hybrid technique for leak detection and location in straight pipelines,” Journal of Loss Prevention in the Process Industries, vol. 35, pp. 157–168, 2015.

[4] T. R. Sheltami, A. Bala, and E. M. Shakshuki, “Wireless sensor networks for leak detection in pipelines: a survey,” Journal of Ambient Intelligence and Humanized Computing, vol. 7, no. 3, pp. 347–356, 2016.

[5] J. Zhang, A. Hoffman, K. Murphy, J. Lewis, M. Twomey et al., “Review of pipeline leak detection technologies,” in PSIG Annual Meeting. Pipeline Simulation Interest Group, 2013.

[6] M. Golmohammadi, “Pipeline leak detection,” 2015

[7] M. F. Sulaima, A. Faizal, M. H. Jali, W. Daud, W. M. Bukhari, M. Nasir, M. Na’im, and M. F. Baharom, “A feasibility study of internal and external based system for pipeline leak detection in upstream petroleum industry,” Australian Journal of Basic and Applied Sciences, pp. 204– 210, 2014.

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