A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

Rajasekaran, Uma; Kothandaraman, Mohanaprasad

doi:10.1061/jpsea2.pseng-1611

Cited by 7 publications

(2 citation statements)

References 108 publications

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“…Attempts are made to predict network failure rates using mathematical and numerical methods (including machine learning, artificial neural networks, fuzzy sets, and genetic algorithms, e.g., as shown in [18][19][20][21][22][23][24][25]). Increasingly better ways of detecting and locating leaks as well as methods of limiting them are being investigated, e.g., as shown in [26][27][28][29][30][31][32][33][34][35][36]. Both direct, using the latest technologies, and indirect methods, based on water loss coefficients, for assessing the technical conditions of water pipes are constantly being improved, e.g., as in [37][38][39][40].…”

Section: The Problem Of Water Supply Network Failures Resulting In Le...mentioning

confidence: 99%

Fractal Characteristics of Water Outflows on the Soil Surface after a Pipe Failure

Iwanek,

Suchorab

2024

Water

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Water pipe failures result in real water losses in the form of water outflowing into the porous medium, such as the surrounding soil. Such an outflow may result in the creation of suffosion holes. The appropriate management of the water supply network may contribute to reducing the number of failures, but due to their random nature, it is not possible to completely eliminate them. Therefore, alternative solutions are being sought to reduce the effects of the failures. This article presents a fragment of the results from a broader scope of the research, which attempted to determine the outflow zone in relation to the fractal characteristics of water outflows. The research included the analysis of the actual geometric structures created by the water outflows, which were simplified into linear structures using isometric transformations. The structures were analyzed in terms of the parameters characterizing them, including their fractal dimensions. As a result, it was found that there was no relationship between the analyzed fractal parameters and the leakage area or hydraulic pressure in the water pipe. However, the influence of the number of points forming each linear structure on the analyzed parameters was shown. This allowed for the determination of further research aimed at estimating the size of the water outflow zone after the unsealing of an underground water supply pipe.

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Section: The Problem Of Water Supply Network Failures Resulting In Le...mentioning

confidence: 99%

Fractal Characteristics of Water Outflows on the Soil Surface after a Pipe Failure

Iwanek,

Suchorab

2024

Water

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“…Finally, the results of this study are compared with the existing research results. For example, Rajasekaran & Kothandaraman (2024) used a similar BPNN model to classify the leakage noise of urban gas pipelines, but its highest accuracy was only 92%, and the performance of the model was better than their research. This may be attributed to the adoption of PCA technology in the feature extraction stage, which effectively reduces the dimension of input features, thus reducing the computational burden of the model and improving the performance of the classifier.…”

Section: Signal Feature Extraction and Pattern Recognitionmentioning

confidence: 98%

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background

Liu,

Lu,

Wen

et al. 2024

PeerJ Computer Science

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The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

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An early warning method of pipeline leakage monitoring with limited leakage samples

Cai,

Wang,

et al. 2025

Measurement

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A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

Cited by 7 publications

References 108 publications

Fractal Characteristics of Water Outflows on the Soil Surface after a Pipe Failure

Fractal Characteristics of Water Outflows on the Soil Surface after a Pipe Failure

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background

An early warning method of pipeline leakage monitoring with limited leakage samples

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