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Review

Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review

1
Department of Information Technology, University CEU San Pablo, 28003 Madrid, Spain
2
Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain
3
FOCUS S.L., 28004 Madrid, Spain
4
Instituto de Óptica, CSIC, 28006 Madrid, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2017, 7(8), 841; https://doi.org/10.3390/app7080841
Received: 19 July 2017 / Revised: 7 August 2017 / Accepted: 10 August 2017 / Published: 16 August 2017
(This article belongs to the Special Issue Distributed Optical Fiber Sensors)
There is an increasing interest in researchers and companies on the combination of Distributed Acoustic Sensing (DAS) and a Pattern Recognition System (PRS) to detect and classify potentially dangerous events that occur in areas above fiber optic cables deployed along active pipelines, aiming to construct pipeline surveillance systems. This paper presents a review of the literature in what respect to machine learning techniques applied to pipeline surveillance systems based on DAS+PRS (although its scope can also be extended to any other environment in which DAS+PRS strategies are to be used). To do so, we describe the fundamentals of the machine learning approaches when applied to DAS systems, and also do a detailed literature review of the main contributions on this topic. Additionally, this paper addresses the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring, and intends to provide useful insights and recommendations in what respect to the design of such systems. The literature review concludes that a real field deployment of a PRS based on DAS technology is still a challenging area of research, far from being fully solved. View Full-Text
Keywords: distributed acoustic sensing; fiber optic systems; ϕ-OTDR; pipeline integrity threat monitoring; pattern recognition systems; review distributed acoustic sensing; fiber optic systems; ϕ-OTDR; pipeline integrity threat monitoring; pattern recognition systems; review
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MDPI and ACS Style

Tejedor, J.; Macias-Guarasa, J.; Martins, H.F.; Pastor-Graells, J.; Corredera, P.; Martin-Lopez, S. Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review. Appl. Sci. 2017, 7, 841. https://doi.org/10.3390/app7080841

AMA Style

Tejedor J, Macias-Guarasa J, Martins HF, Pastor-Graells J, Corredera P, Martin-Lopez S. Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review. Applied Sciences. 2017; 7(8):841. https://doi.org/10.3390/app7080841

Chicago/Turabian Style

Tejedor, Javier, Javier Macias-Guarasa, Hugo F. Martins, Juan Pastor-Graells, Pedro Corredera, and Sonia Martin-Lopez. 2017. "Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review" Applied Sciences 7, no. 8: 841. https://doi.org/10.3390/app7080841

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