Leak Detection Systems

Bai, Yong; Bai, Qiang

doi:10.1016/b978-0-12-394432-0.00006-8

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…This non-computationally complex method makes the implementation of PPA easy. However, this technique cannot be used to localise leakage without combining it with other techniques (e.g., MVB) [51].…”

Section: Pressure Point Analysis (Ppa)mentioning

confidence: 99%

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Ahmed

Mouël

Stouls

et al. 2023

Sensors

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Crude oil leakages and spills (OLS) isare some of the problems attributed to pipeline failures in the oil and gas industry’s midstream sector. Consequently, they are monitored via several leakage detectiondetection and localisation techniques (LDTs) comprising classical methods and, recently, Internet of Things (IoT)-based systems via wireless sensor networks (WSNs). Although the latter techniques are proven to be more efficient, they are susceptible to other types of failures such as high false alarms or single point of failure (SPOF) due to their centralised implementations. Therefore, in this work, we present a hybrid distributed leakage detection and localisation technique (HyDiLLEch), which combines multiple classical LDTs. The technique is implemented in two versions, a single-hop and a double-hop version. The evaluation of the results is based on the resilience to SPOFs, the accuracy of detection and localisation, and communication efficiency. The results obtained from the placement strategy and the distributed spatial data correlation include increased sensitivity to leakage detection and localisation and the elimination of the SPOF related to the centralised LDTs by increasing the number of node-detecting and localising (NDL) leakages to four and six in the single-hop and double-hop versionversions, respectively. In addition, the accuracy of leakages is improved from 0 to 32 m in nodes that were physically close to the leakage points while keeping the communication overhead minimal.

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Section: Pressure Point Analysis (Ppa)mentioning

confidence: 99%

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Ahmed

Mouël

Stouls

et al. 2023

Sensors

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“…Most detection techniques rely on the measurement of a physical quantity or reaction to a physical phenomenon, such as the acoustic, flow rate, pressure, gas sampling, optics, etc., and their combinations [ 5 , 6 , 7 , 8 , 9 ]. Among the current fault diagnosis methods for the oil and gas pipeline leakage, some are only suitable for the specific operating environment (e.g., soil–gas sampling method [ 10 ]) or mainly designed for gas leakage (e.g., ultrasonic flowmeter [ 11 ]); moreover, some sensors are difficult to install and the maintenance is costly (e.g., distributed optical fiber sensor method [ 12 ] and cable sensor [ 13 ]).…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals

Xie

Xiao

Liu

et al. 2020

Sensors

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Detection technology of underwater pipeline leakage plays an important role in the subsea production system. In this paper, a new method based on the acoustic leak signal collected by a hydrophone is proposed to detect pipeline leakage in the subsea production system. Through the pipeline leakage test, it is found that the radiation noise is a continuous spectrum of the medium and high-frequency noise. Both the increase in pipe pressure and the diameter of the leak hole will narrow the spectral structure and shift the spectrum center towards the low frequencies. Under the same condition, the pipe pressure has a greater impact on the noise; every 0.05 MPa increase in the pressure, the radiation sound pressure level increases by 6-7 dB. The time-frequency images were obtained by processing the acoustic signals using the Ensemble Empirical Mode Decomposition (EEMD) and Hilbert–Huang transform (HHT), and fed into a two-layer Convolutional Neural Network (CNN) for leakage detection. The results show that CNN can correctly identify the degree of pipeline leakage. Hence, the proposed method provides a new approach for the detection of pipeline leakage in underwater engineering applications.

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“…There are over 4 million kilometers (over 600 thousand kilometers in China) of pipelines buried under the Earth and marine seabed, and the diameter of at least 1/3 of the total pipelines is less than 12 inches (small-diameter pipeline, D<30cm) [ 4 , 5 , 6 ]. Generally, Pipeline Integrated Management (PIM) should be strictly conducted to ensure that the operating pipeline has a safety rate of 99.9%, computed accordingly to [ 7 , 8 ]. Corrosion, human activities, long-term Earth movement and natural disasters are the main reasons for an operation pipeline to be cracked, crooked, leakage or in the event of an explosion [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying

et al. 2020

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It is of great importance for pipeline systems to be is efficient, cost-effective and safe during the transportation of the liquids and gases. However, underground pipelines often experience leaks due to corrosion, human destruction or theft, long-term Earth movement, natural disasters and so on. Leakage or explosion of the operating pipeline usually cause great economical loss, environmental pollution or even a threat to citizens, especially when these accidents occur in human-concentrated urban areas. Therefore, the surveying of the routed pipeline is of vital importance for the Pipeline Integrated Management (PIM). In this paper, a comprehensive review of the Micro-Inertial Measurement Unit (MIMU)-based intelligent Pipeline Inspection Gauge (PIG) multi-sensor fusion technologies for the transport of liquids and gases purposed for small-diameter pipeline (D<30cm) surveying is demonstrated. Firstly, four types of typical small-diameter intelligent PIGs and their corresponding pipeline-defects inspection technologies and defects-positioning technologies are investigated according to the various pipeline defects inspection and localization principles. Secondly, the multi-sensor fused pipeline surveying technologies are classified into two main categories, the non-inertial-based and the MIMU-based intelligent PIG surveying technology. Moreover, five schematic diagrams of the MIMU fused intelligent PIG fusion technology is also surveyed and analyzed with details. Thirdly, the potential research directions and challenges of the popular intelligent PIG surveying techniques by multi-sensor fusion system are further presented with details. Finally, the review is comprehensively concluded and demonstrated.

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Leak Detection Systems

Cited by 7 publications

References 1 publication

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals

A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying

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