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

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

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

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

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI