Adaptive filter signal pressure detector in high density polyethylene water distribution networks to detect and locate leaks

https://doi-001.org/1025/17676264533007

Miloud Bentoumi 1, Ahmed Bentoumi 2

1 Laboratory of System Signal Analysis (LASS), Department of Electronics, Faculty of Technology, University of M’sila, University Pole, Road Bordj Bou Arreridj, M’sila 28000, Algeria, Email: miloud.bentoumi@univ-msila.dz

2 University of M’sila University Pole, Road Bordj Bou Arreridj, M’sila 28000, Algeria, Email:

ahmed_bentoumi@yahoo.fr

Received : 29/12/2025 ; Published: 05/01/2026

Abstract

The currently sold leak detectors worldwide are based on the analysis of acoustic emissions from pipeline leaks. Unfortunately, they not only detect pipeline leakage signals but also the noise from daily human activities and road traffic. These usually cause false alarms, resulting in damage to infrastructures when using these devices with inexperienced agents. In this study, the authors propose as a first contribution pressure transmitters, whose information is not influenced by environmental noise. In addition, for an arbitrarily chosen position of the pressure sensor, an analysis is carried out on the denoising quality as a function of the filter parameters for different denoising filters like EMD (empirical mode decomposition) and wavelet (WT). In our case, we opted for the adaptive filtering Kalman which presents a better metric signal-to-noise ratio of 46 dB like WT and EMD denoising. A novel detector based on adaptive filter signal pressure (AFSPD) is proposed to detect a leak. Many methods are utilized to know the leak’s position, which generally depends on the proposed mathematical model. In our work two methods delay time and numerical cross-correlation are used to locate the leak relative to one of the transducers. In doing this, the validation of the detection and localization technique is confirmed by previously known positions, on the designed PEHD prototype pipe with a diameter of 40mm and a length of 100m. Furthermore, an acquisition system is based essentially on a DSpace professional research acquisition card and pressure transducers installed on the prototype pipeline.

KEYWORDS: WDNs, Pressure transducer, Kalman Filter, Leakage detection,Localization.

  1. Introduction

Water distribution is an essential component of urban infrastructure, and is managed by government bodies or utilities responsible for ensuring that water is supplied regularly and safely to consumers [1]. This process generally involves the construction and maintenance of pipes, storage reservoirs pumping stations, and water quality control devices [2]. A certain amount of pressure is required to circulate water through the pipes [3]. If the latter decreases to a certain value (threshold), and with the existence of a leak, this can engender contamination of the public [4]. Water pressure in the distribution network can also cause damage to infrastructure, as well as significant water losses that are costly for the company. High water pressure can exert a force on pipe walls, causing them to crack or break. Leaks can also be caused by accidents such as construction work in the vicinity of the distribution network [5]. These problems can degrade the quality of the water and cause health problems for those who use it. In addition, leaks lead to waste of drinking water which has become so precious, which has economic and environmental implications [6]. Water leak detection is the process of confirming and identifying leaks in water distribution systems. This can be achieved by using specialized such as modern or conventional water leak detectors. These devices can detect several parameters, such as changes in water pressure, noise, temperature, or flow rate, which can help to indicate the presence and location of a leak [7]. In this work, we thought to solve the leakage problem by changing the acoustic sensors which have the disadvantage of capturing the surrounding noise in general. The latter is detected by the majority of current acoustic and vibration detectors and generally causes false alarms annoying the infrastructures by the use of pressure transmitters [8]. These have the advantage of being very precise, and their information is not influenced by environmental noise [9]. To this end, we designed a new prototype pipe 100 meters long and 40 mm in diameter, on which our transducers are installed, as well as an acquisition system based essentially on a DSpace MicroLabBox professional research acquisition card [10]. The information given by the transducers is established by an integrated 4-20mA current loop [11].  Furthermore, the information transmitted by devices is always immersed in noise, whatever their type (digital or analog) [12]. For this, filtering (denoising) is essential. Several filtering techniques are available. We studied for a single distance from the second pressure transducer the effect of filtering parameters on the denoising of our signal using the SNR metric. At first, in the case of denoising by the KALMAN filter, we studied the effect of the variation of statistical quantities such as covariance of the signal on the efficiency of the filtering by calculating the SNR metric parameter. We fixed the covariance of the process noise which describes the error in predicting the future state of the system and varied the covariance of the measurement noise which describes the error in actually measuring the state of the system and vice versa. We also investigated wavelet denoising by initially using a specific type of Daubechies wavelet, adjusting the wavelet level, and then calculating the signal-to-noise ratio (SNR).

Next, we maintained a constant level while varying the wavelet order from Db2 to Db14. Finally, we systematically altered the wavelet type and computed the SNR. Ultimately, we applied the EMD technique to denoise our signal, which involves breaking the signal down into intrinsic mode functions (IMFs) and a residue. We discarded the first IMFs that correspond to high-frequency signals while retaining the low-frequency signals. As a result, we achieved an SNR of approximately 46 dB at a distance of 76 meters. In our work, we opted for adaptive filtering (Kalman filter) which offers also a better signal-to-noise ratio like wavelet and EMD denoising [13]. A threshold of T=10% of the maximum value of the signal at the location of the transmitters is used to determine whether a leak is present. We used two methods to locate the leakage point of one of the transducers. Validation of the detection and localization technique is confirmed by previously known positions.

  1. Background on denoising methods

We present in this section the filtering methods which are used in this paper.

  1. Wavelet method

In signal processing and mathematics, a wavelet is a mathematical tool used for analyzing the frequency content of signals. In other words, Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information [14]. One major advantage afforded by wavelets is the ability to perform local analysis. Wavelet analysis is capable of revealing aspects of data that other signal analysis techniques miss, aspects like trends, breakdown points, discontinuities in higher derivatives, and self-similarity. Wavelet analysis can often compress or de-noise a signal without appreciable degradation. A wavelet is a waveform of effectively limited duration that has an average value of zero. CWT is defined as the sum over all time of the signal multiplied by scaled, shifted versions of the wavelet function .

             (1)

The basic mathematical representation of a wavelet involves dilation and translation of a mother wavelet function. One commonly used wavelet function is the Morlet wavelet, often used in continuous wavelet transforms. The Morlet wavelet is defined as a complex exponential modulated by a Gaussian function.

The Morlet wavelet function can be expressed as follows:

                          (2)

Where:   is the wavelet function at time t. A is a normalization constant. i is the imaginary unit. f0 is the nondimensional frequency parameter that controls the number of oscillations within the wavelet.  is the standard deviation of the Gaussian window, controlling the width of the wavelet in the time domain.

The continuous wavelet transforms of a signal using the Morlet wavelet is given by the convolution of with the scaled and translated wavelet:

                   (3)

a: represents the dilation (scaling) parameter. b: represents the translation parameter. ψ∗ is the complex conjugate of the wavelet function

This integral is computed for various values of a and b to analyze the signal at different scales and positions in time.

Conclusion

In this work, we proposed an adaptive filter signal pressure detector (AFSPD) based on pressure transducers. Many filtering techniques are applied one is adaptive based on the Kalman filter and the other is based on wavelet transform and EMD. The quality of the filtering techniques adopted was studied based on signal-to-noise ratio (SNR). The results obtained are closer to the experimental values. They contain an error linked to the effective distance. The detector showed its effectiveness on a prototype pipeline.

References

https://doi.org/10.1016/j.rineng.2023.101395

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 http://dx.doi.org/10.1016/j.physa.2014.01.020

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  • Jian Li, Shili Chen, Yu Zhang, Shijiu Jin, Likun Wang ‘Cross-correlation Method for Online Pipeline Leakage Monitoring System’, IEEE conference 2009 2nd International Congress on Image and Signal Processing (CISP 2009), 17-19 October 2009, Tianjin, China, DOI: 10.1109/CISP.2009.5302839
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