A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe

Kim, Youngseok; Jung, Hae-Wook; Ryou, Jae-Suk; Choi, Jaehyuk

doi:10.3390/app11052097

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The microcontroller is responsible of running the processing tasks for extracting some relevant features from a temporal sequence of measurements and classifying the sequence as a leakage/non-leakage event. Indeed, as discussed in [12] by Youngseok et al, the spectrum of the vibration signal in case of regular functioning of the pipe is quite different from the one acquired during the leakage: in presence of leakages, there are some frequential components in the range from 400 Hz to 5 kHz, according to the entity of the leakage and the dimension and material of the pipe. The amplitude of the sensed vibration varies based on the distance between the leakage and the sensor and it can change depending on the amount of wasted water.…”

Section: Leakage Detection Using Vibration Signals and Ai On Boardmentioning

confidence: 99%

A distributed analysis of vibration signals for leakage detection in Water Distribution Networks

Restuccia

2023

Materials Research Proceedings

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Abstract. It is well known that Water Distribution Networks (WDNs) are very inefficient and, in Italy, 40% of water is lost during distribution. In this paper, we present a solution for detecting leakages in WDNs, based on three main components: i) an innovative sensing element to be deployed at the sensor nodes, which analyses vibrations in the acoustic range for classifying external noise sources, induced by water leakages, by means of suitable machine learning techniques; ii) an Internet of Things (IoT) system of sensors, deployed at the junctions of the WDNs, for comparing the measurements collected at different critical points of the network; iii) a machine learning algorithm for processing the data. After the definition of the WDN structure, we introduce some numerical simulation tools suitable for studying our system and modeling the proposed sensing solution. Given the geometry, physical properties (pipe lengths, diameters, roughness, reservoir shapes and levels, pump and valve characteristic curves) and nodal demands, the simulation tool is able to compute leakages in pipes or nodes over time. In parallel, we simulate our IoT system coupled to the WDN, by logging partial information about the WDN status, which corresponds to the demand readings at the edge nodes or at some junction nodes, together with the (optional) measurements of the deployed sensing elements. On the basis of this data, we analyze the possibility of identifying the leakages in the network, even without knowing the exact or complete topology of the WDN. Our solution exploits different machine learning techniques devised to indirectly retrieve topological information, by correlating the balance of the flows as the water demand varies over time.

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Section: Leakage Detection Using Vibration Signals and Ai On Boardmentioning

confidence: 99%

A distributed analysis of vibration signals for leakage detection in Water Distribution Networks

Restuccia

2023

Materials Research Proceedings

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“…In these cases, the basic correlation technique described in [16] is insufficient to correctly estimate the lag time. With these cases, more complex signal processing algorithms are required, as, for example, time-frequency analysis [10,17]. This article describes the use of wavelet-cross-correlation (WCC) analysis for processing signals acquired in the practical study of water supply networks.…”

Section: Introductionmentioning

confidence: 99%

Towards applicability of wavelet-based cross-correlation in locating leaks in steel water supply pipes

Faerman

Sharkova²,

Avramchuk³

et al. 2022

J. Phys.: Conf. Ser.

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The paper discussed the application of wavelet-based cross-correlation analysis to the processing of the leak noise signals acquired in the field. Nine recordings produced with the commercial leak noise correlator are considered in this study. All the cases are considered as classified as challenging for signal interpretation. For most of the cases, the basic correlation technique proved to be insufficient to measure the time lag with the required confidence. We implemented a wavelet cross-correlation algorithm for time lag estimation. At least in five cases out of nine, it managed to estimate time lag accurately. In three of the remaining cases, it failed. Unfortunately, due to a lack of information on field studies when records were produced, we are not able to investigate the reason of failures. We concluded that the proposed solution is practical and can be implemented in the software of leak noise correlators.

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Smart self-sensory TRC pipes—proof of concept

Perry¹,

Goldfeld²

2022

Smart Mater. Struct.

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The paper aims to prove the feasibility of smart concrete pipe systems with integrated monitoring capabilities. The development of such systems is motivated by functional, structural, sustainability and monitoring requirements of underground and buried pipelines with limited accessibility. To answer these challenges, the study adopts the textile reinforced concrete (TRC) technology that allows the production of effective, durable, and lightweight structural elements with integrated monitoring systems. In such systems, by utilizing the electrical conductivity of the carbon rovings, the carbon-based textile can simultaneously serve as the main reinforcement system and as the sensory agent. The proposed hybrid monitoring system aims to detect the occurrence of leakage and to distinguish its severity, which is directly correlated to the structural health. Smart TRC pipes were designed, constructed, and experimentally investigated from structural and sensory points of view. The design considers the multifunctionality of the carbon rovings and the hybrid performance of the textile cage, from both aspects - reinforcement and sensing. An experimental investigation explores the mutual structural, functional, and sensory capabilities of the hybrid system, which reflect and affect each other. It is presented that the strong correlation between the structural-functional and sensory responses reveals an efficient smart TRC pipes. The presented results take a major step toward the realization of the smart TRC concept and exceeded beyond small 1D scale elements to 3D structures.

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A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe

Cited by 4 publications

References 18 publications

A distributed analysis of vibration signals for leakage detection in Water Distribution Networks

A distributed analysis of vibration signals for leakage detection in Water Distribution Networks

Towards applicability of wavelet-based cross-correlation in locating leaks in steel water supply pipes

Smart self-sensory TRC pipes—proof of concept

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