A review on different pipeline fault detection methods

Datta, Shantanu; Sarkar, Shibayan

doi:10.1016/j.jlp.2016.03.010

Cited by 310 publications

(158 citation statements)

References 47 publications

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“…By contrast, there are many developed technologies studied to detect leakages occurring at high‐pressure continuous gas pipe and hydraulic pipe, which can be used for reference. Those technologies include software‐based methods, such as volume–balance method, negative pressure wave method, transient model method, and hardware‐based methods, such as distributed optical fiber method and acoustic emission (AE) method . Among them, transient pressure wave‐based methods were exploited to detect and locate faults and leakages on different types of water pipes, and it was used to settle multidefects detection and complex pipeline connection situations .…”

Section: Introductionmentioning

confidence: 99%

“…Those technologies include software-based methods, such as volume-balance method, negative pressure wave method, transient model method, and hardware-based methods, such as distributed optical fiber method and acoustic emission (AE) method. 4,5 Among them, transient pressure wave-based methods were exploited to detect and locate faults and leakages on different types of water pipes, [6][7][8][9] and it was used to settle multidefects detection and complex pipeline connection situations. 10,11 But the pressure peaks can potentially damage the pipes.…”

Section: Introductionmentioning

confidence: 99%

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Experimental validation of gas leak detection in screw thread connections of galvanized pipe based on acoustic emission and neural network

Gong

Song

2019

Struct Control Health Monit

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Summary Galvanized steel pipes with screw thread connections are widely used in the household part of urban gas distribution system. For such pipes, leakages usually occur at the connections as opposed to the pipe bodies. A leak detection method has been proposed for galvanized steel pipes on the basis of acoustic emission (AE) and neural network. From the viewpoint of engineering application, this work conducts a thorough experimental investigation on the efficiency, accuracy, and applicability conditions of the method. An experimental platform consisting of one trunk pipe and three branch pipes with different diameters is set up to simulate the various leak scenarios. After feature extraction and analysis of the AE signals, a classifier on the basis of back propagation neural network is built for gas leak identification. The applicability of the classifier is investigated quantitatively considering different pipe diameters, number of connections, and leak rate. It validates that the proposed leak detection model can achieve an average accuracy above 95% of the leak with flow above 0.052 L/s on the trunk pipe, on the premise that the signals pass through no more than four screw thread connections. Then, the leak with flow above 0.1 L/s on branch pipes can be detected under the same sensor arrangement.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental validation of gas leak detection in screw thread connections of galvanized pipe based on acoustic emission and neural network

Gong

Song

2019

Struct Control Health Monit

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“…Because of aging pipelines, corrosion, or welding defects, small leaks and slow leaks occur frequently; such leaks represent risks to the environment and can cause financial losses [1][2][3][4]. The pressure drop produced by a small leakage is low and difficult to detect; however, small leak and slow leak are main forms of leakage of long distance oil pipelines during the service period.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

Lang

et al. 2018

Wireless Communications and Mobile Computing

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The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBN ICR ) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBN ICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

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“…Pipeline transportation is widely used in oil and gas transportation because of its safety, reliability and economy [1,2]. However, pipeline leakage can cause environment pollution and financial losses.…”

Section: Introductionmentioning

confidence: 99%

Leak Location of Pipeline with Multibranch Based on a Cyber-Physical System

Lang

et al. 2017

Information

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Abstract:Data cannot be shared and leakage cannot be located simultaneously among multiple pipeline leak detection systems. Based on cyber-physical system (CPS) architecture, the method for locating leakage for pipelines with multibranch is proposed. The singular point of pressure signals at the ends of pipeline with multibranch is analyzed by wavelet packet analysis, so that the time feature samples could be established. Then, the Fischer-Burmeister function is introduced into the learning process of the twin support vector machine (TWSVM) in order to avoid the matrix inversion calculation, and the samples are input into the improved twin support vector machine (ITWSVM) to distinguish the pipeline leak location. The simulation results show that the proposed method is more effective than the back propagation (BP) neural networks, the radial basis function (RBF) neural networks, and the Lagrange twin support vector machine.

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A review on different pipeline fault detection methods

Cited by 310 publications

References 47 publications

Experimental validation of gas leak detection in screw thread connections of galvanized pipe based on acoustic emission and neural network

Experimental validation of gas leak detection in screw thread connections of galvanized pipe based on acoustic emission and neural network

Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

Leak Location of Pipeline with Multibranch Based on a Cyber-Physical System

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