Real time leak detection system applied to oil pipelines using sonic technology and neural networks

Avelino, Alvaro Medeiros; Paiva, José Alvaro de; Silva, Rodrigo E. F. da; Araujo, Gabriell J. M. de; Azevedo, Fabiano M. de; Quintaes, Filipe de O.; Maitelli, André Laurindo; Neto, Adrião Duarte Dória; Salazar, Andrés O.

doi:10.1109/iecon.2009.5415324

Cited by 22 publications

(9 citation statements)

References 4 publications

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“…Therefore, a continuous inspection of the pipeline condition is important to detect leaks. Over the past few decades, numerous external and internal monitoring methods have been proposed for pipeline leak detection, including negative pressure wave (NPW) techniques [1], accelerometerbased techniques [2], acoustic emission (AE) technology [3], time-domain reflectometry [4], distributed temperature sensing systems [5], ultrasonic technology [6], and magnetic flux leakage techniques [7]. AE technologies have gained significant popularity because of its rapid leak detection capability, high sensitivity, real-time response, and ease of retrofitting [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov–Smirnov Test

Rai

Ahmad

Hasan

2021

Sensors

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Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis.

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Section: Introductionmentioning

confidence: 99%

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov–Smirnov Test

Rai

Ahmad

Hasan

2021

Sensors

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“…En otras áreas, como es el caso del transporte del petróleo, transporte de agua o el proceso de la refinación del petróleo, se encuentran muchas técnicas de procesamiento digital aplicadas a la detección de fugas. Algunas de ellas son: redes neuronales [3][4], wavelets [4][5], teoría de control difuso [6][7][8], entropía [1], Lyapunov [11], autocorrelación [1][2], teoría de caos [9], predictor ARX [10], interferometría óptica [12][13], entre otras.…”

Section: Introductionunclassified

Pruebas de estanqueidad en envases de tereftalato de polietileno basado en máquina de soporte vectorial

Sierra

Amaya

Sánchez

2015

Ingeniare. Rev. chil. ing.

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“…In these sensors the detection mechanism is through a mechanical or acoustic wave. As the acoustic wave propagates through the material, any change in the signal propagation characteristics (as a leak in a pipeline) affects the speed and/or amplitude of the wave [6].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Flexible Gas Pipeline with a Microphone and Artificial Neural Networks

Sousa¹,

Santos²,

Silva³

et al. 2013

Power and Energy / 807: Intelligent Systems and Control / 808: Technology for Education and Learning

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Pipeline networks are complex systems of ducts transporting gas and chemical products through long distances. With the purpose to track these leaks a technique, based on the analysis of sound noises captured by a microphone and on pressure transients generated by leak occurrence, was developed. Neural Artificial Networks were applied to determine leak magnitude and leak location. The experimental results showed that it is possible to detect leaks in pipelines. The dynamics of these noises in time were used as input to the neural model to determine the location and magnitude of the leaks.

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Real time leak detection system applied to oil pipelines using sonic technology and neural networks

Cited by 22 publications

References 4 publications

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov–Smirnov Test

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov–Smirnov Test

Pruebas de estanqueidad en envases de tereftalato de polietileno basado en máquina de soporte vectorial

Monitoring Flexible Gas Pipeline with a Microphone and Artificial Neural Networks

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