A comprehensive review of acoustic based leak localization method in pressurized pipelines

Hu, Zhongyu; Tariq, Salman; Zayed, Tarek

doi:10.1016/j.ymssp.2021.107994

Cited by 90 publications

(18 citation statements)

References 97 publications

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“…All these applications are capable of creating and depicting basic bibliometric networks; however, VOSviewer was selected for our study due to the ease of use, easy interpretability of networks through visualization, and the popularity of the software in engineering and management research (Tariq et al 2020). Science mapping analysis has already been employed in different research areas, such as construction safety (Akinlolu et al 2020), megaprojects (Wang et al 2020), and acoustic-based leak localization (Hu et al 2021), which has yielded meaningful research outcomes including (1) working linkages and measurements between researchers, countries, and organizations and (2) preliminary ideas on the existing research themes and future research directions (Tariq et al 2020). In our study, VOSviewer was used for science mapping of journal sources, contributing countries, and popular keywords.…”

Section: Science Mapping Analysismentioning

confidence: 99%

“…Water leakage is noted to be the primary cause of this water loss (Zaman et al 2020) and, in some cases, may even result in 50% of the total water loss (Kanakoudis 2004;Tariq et al 2020). Nevertheless, it is impossible to avoid leakages in the WDNs completely (Hu et al 2021); however, the need to adopt reliable methods and techniques to detect and locate leaks immediately cannot be overstated.…”

Section: Introductionmentioning

confidence: 99%

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Application of fiber optics in water distribution networks for leak detection and localization: a mixed methodology-based review

et al. 2021

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This study reviews the state-of-the-art application of fiber optics in water distribution networks for leak detection and localization. The use of fiber optics in the oil and gas sector has been well-established; however, its potential in water pipelines is not evident owing to limited research. This study, therefore, presents the research developments of fiber optics in water leak detection and localization using the mixed methodology approach by integrating bibliometric and systematic analyses. A scientometric analysis is carried out to analyze the science maps of (1) journal sources, (2) contributing countries, and (3) co-occurrence of influential keywords. The systematic analysis evaluates the use of eight types of fiber optics, such as accelerometer-based fiber optics and hydrophone-based fiber optics, in water leak detection and localization. The review reveals five important directions for future research such as real network-based studies and the development of hybrid techniques.

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Section: Science Mapping Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of fiber optics in water distribution networks for leak detection and localization: a mixed methodology-based review

et al. 2021

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“…Pipelines play an important role in the distribution of liquid and gas resources. However, a leak in a pipeline can lead to severe consequences such as wasted resources, distribution downtime, risks to community health, and economic losses [1]. To avoid these consequences, early leak detection is important.…”

Section: Introductionmentioning

confidence: 99%

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

Ahmad

Kim

et al. 2022

Sensors

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This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To obtain leak-related features, first, acoustic images were obtained from the time series acoustic emission signals using continuous wavelet transform. The acoustic images (AE images) were the wavelet scalograms that represent the time–frequency scales of the acoustic emission signal in the form of an image. The acoustic images carried enough information about the leak, as the leak-related information had a high-energy representation in the scalogram compared to the noise. To extract leak-related discriminant features from the acoustic images, they were provided as input into the convolutional autoencoder and convolutional neural network. The convolutional autoencoder extracts global features, while the convolutional neural network extracts local features. The local features represent changes in the energy at a finer level, whereas the global features are the overall characteristics of the acoustic signal in the acoustic image. The global and local features were merged into a single feature vector. To identify the pipeline leak state, the feature vector was fed into a shallow artificial neural network. The proposed method was validated by utilizing a data set obtained from the industrial pipeline testbed. The proposed algorithm yielded a high classification accuracy in detecting leaks under different leak sizes and fluid pressures.

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“…Such machinery can be targeted for noise reduction, where its noise footprint can be analyzed and compared between diverse workflows or product life spans [27]. In the context of predictive maintenance, one can find applications for preventing structural failure [28], leak localization [29], or nondestructive localization of cracks [30]. In its early stages, the PSO algorithm [9,10] was also used for solving some acoustic localization problems, namely, those related to the localization of partial discharge sources in power transformers [31,32].…”

Section: Introductionmentioning

confidence: 99%

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Fé

Correia

Tomic

et al. 2022

Sensors

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In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.

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A comprehensive review of acoustic based leak localization method in pressurized pipelines

Cited by 90 publications

References 97 publications

Application of fiber optics in water distribution networks for leak detection and localization: a mixed methodology-based review

Application of fiber optics in water distribution networks for leak detection and localization: a mixed methodology-based review

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

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