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 con...
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| Published in | Applied sciences Vol. 7; no. 8; p. 841 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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MDPI AG
16.08.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2076-3417 2076-3417 |
| DOI | 10.3390/app7080841 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Martins, Hugo Macias-Guarasa, Javier Tejedor, Javier Pastor-Graells, Juan Corredera, Pedro Martin-Lopez, Sonia |
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| Cites_doi | 10.1364/ACPC.2015.ASu2A.145 10.1109/OFC.2007.4348619 10.1109/LPT.2011.2182643 10.1117/12.2192075 10.1109/JLT.2014.2308354 10.1016/S0924-0136(02)00160-7 10.1109/JLT.2015.2421953 10.1016/j.measurement.2009.12.022 10.1364/OL.30.003284 10.1364/OE.24.000853 10.1016/j.engstruct.2004.05.018 10.1364/OE.20.020459 10.1109/JLT.2013.2273553 10.1117/12.754927 10.1364/AO.46.001968 10.1364/OE.24.022303 10.1364/FIO.2015.FTh4E.1 10.1016/j.sna.2007.02.012 10.3390/s17020355 10.1109/JLT.2015.2396359 10.1117/12.607175 10.1109/JLT.2005.849924 10.1201/b16868 10.1109/JLT.2016.2542981 10.1364/OE.22.013804 10.21437/Interspeech.2004-337 10.1016/j.sna.2004.02.034 10.1364/OL.39.004313 10.1016/j.sna.2004.03.068 10.1109/ICIRT.2016.7588715 10.3390/s150715179 10.1109/CISE.2009.5365100 10.1109/WGEC.2009.117 10.1006/mssp.2001.1462 10.1016/S0304-3894(03)00199-7 10.1109/79.985674 10.3390/s130708534 10.1016/j.optlaseng.2008.07.002 10.1117/12.484911 10.1109/ICIECS.2009.5364602 10.1093/oso/9780199214655.003.0001 10.1109/ICIEA.2006.257218 10.1364/OL.39.005866 10.1109/JLT.2013.2286223 10.1109/JPROC.2003.814923 10.1364/OE.24.013121 10.1016/S0263-2241(03)00048-4 10.1109/ICSPCom.2013.6719846 10.1109/70.760356 10.1093/ietcom/e91-b.11.3544 10.3390/s150921957 10.1016/j.compind.2007.09.001 10.1109/79.127284 |
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| References | ref_50 Lyons (ref_13) 2003; 34 Quin (ref_33) 2012; 24 ref_58 ref_56 ref_11 ref_55 Wu (ref_27) 2015; 33 ref_53 ref_51 Samanta (ref_62) 2003; 17 Lyons (ref_12) 2002; 127 ref_59 Wang (ref_73) 2016; 24 Wang (ref_5) 2014; 39 Lewis (ref_16) 2007; 136 Wang (ref_37) 2014; 9157 Lyons (ref_14) 2004; 114 ref_61 Sun (ref_21) 2015; 15 Huang (ref_52) 2013; 13 Buerck (ref_9) 2003; 102 ref_25 ref_69 ref_24 Kim (ref_47) 2008; E91-B ref_67 Wu (ref_19) 2014; 9157 ref_22 Rao (ref_30) 2009; 7503 ref_66 ref_65 Martins (ref_72) 2016; 24 Peng (ref_35) 2014; 22 Pan (ref_38) 2014; 9157 Juarez (ref_29) 2007; 46 ref_28 Wang (ref_34) 2014; 39 Quin (ref_41) 2012; 20 Martins (ref_44) 2013; 31 Martins (ref_4) 2014; 32 King (ref_15) 2004; 115 Shi (ref_39) 2015; 15 Martins (ref_40) 2015; 33 ref_71 ref_70 ref_32 ref_75 Kreucher (ref_63) 1999; 15 Juarez (ref_31) 2005; 30 Tejedor (ref_23) 2016; 34 Hlawatsch (ref_64) 1992; 9 Zhang (ref_54) 2012; 8 Zhu (ref_18) 2014; 9062 Babic (ref_68) 2008; 59 Zhu (ref_42) 2013; 31 Li (ref_10) 2004; 26 Juarez (ref_3) 2005; 23 Martins (ref_74) 2016; 24 Li (ref_36) 2014; 9157 ref_46 ref_45 Li (ref_57) 2002; 19 Qu (ref_20) 2010; 43 ref_43 Guo (ref_8) 2009; 47 ref_1 Wu (ref_26) 2014; 91579 ref_2 Brooks (ref_60) 2003; 91 Madsen (ref_17) 2007; 6770 ref_49 ref_48 ref_7 ref_6 |
| References_xml | – ident: ref_46 doi: 10.1364/ACPC.2015.ASu2A.145 – volume: 9157 start-page: 91575Z-1 year: 2014 ident: ref_36 article-title: 124km Phase-sensitive OTDR with Brillouin Amplification publication-title: Proc. SPIE – ident: ref_6 doi: 10.1109/OFC.2007.4348619 – volume: 24 start-page: 542 year: 2012 ident: ref_33 article-title: Wavelet denoising method for improving detection performance of distributed vibration sensor publication-title: IEEE Photonics Technol. Lett. doi: 10.1109/LPT.2011.2182643 – ident: ref_25 doi: 10.1117/12.2192075 – ident: ref_32 – ident: ref_55 – volume: 32 start-page: 1510 year: 2014 ident: ref_4 article-title: Phase-sensitive Optical Time Domain Reflectometer Assisted by First-order Raman Amplification for Distributed Vibration Sensing Over >100 km publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2014.2308354 – volume: 127 start-page: 23 year: 2002 ident: ref_12 article-title: An optical fibre distributed sensor based on pattern recognition publication-title: J. Mater. Process. Technol. doi: 10.1016/S0924-0136(02)00160-7 – volume: 33 start-page: 3156 year: 2015 ident: ref_27 article-title: Separation and Determination of the Disturbing Signals in Phase-Sensitive Optical Time Domain Reflectometry (ϕ-OTDR) publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2015.2421953 – volume: 43 start-page: 513 year: 2010 ident: ref_20 article-title: A SVM-based pipeline leakage detection and pre-warning system publication-title: Measurement doi: 10.1016/j.measurement.2009.12.022 – volume: 30 start-page: 3284 year: 2005 ident: ref_31 article-title: Polarization discrimination in a phase-sensitive optical time-domain reflectometer intrusion-sensor system publication-title: Opt. Lett. doi: 10.1364/OL.30.003284 – volume: 24 start-page: 853 year: 2016 ident: ref_73 article-title: Coherent ϕ-OTDR based on I/Q demodulation and homodyne detection publication-title: Opt. Express doi: 10.1364/OE.24.000853 – ident: ref_65 – ident: ref_61 – ident: ref_1 – volume: 26 start-page: 1647 year: 2004 ident: ref_10 article-title: Recent applications of fiber optic sensors to health monitoring in civil engineering publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2004.05.018 – ident: ref_71 – volume: 20 start-page: 20459 year: 2012 ident: ref_41 article-title: Continuous wavelet transform for nonstationary vibration detection with phase-OTDR publication-title: Opt. Express doi: 10.1364/OE.20.020459 – volume: 31 start-page: 2851 year: 2013 ident: ref_42 article-title: Enhancement of SNR and Spatial Resolution in ψ-OTDR System by Using Two-Dimensional Edge Detection Method publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2013.2273553 – ident: ref_58 doi: 10.1117/12.754927 – volume: 91579 start-page: 915790-1 year: 2014 ident: ref_26 article-title: Field test of a fully distributed fiber optic intrusion detection system for long-distance security monitoring of national borderline publication-title: Proc. SPIE – volume: 46 start-page: 1968 year: 2007 ident: ref_29 article-title: Field test of a distributed fiber-optic intrusion sensor system for long perimeters publication-title: Appl. Opt. doi: 10.1364/AO.46.001968 – volume: 24 start-page: 22303 year: 2016 ident: ref_74 article-title: Real time dynamic strain monitoring of optical links using the backreflection of live PSK data publication-title: Opt. Express doi: 10.1364/OE.24.022303 – ident: ref_11 doi: 10.1364/FIO.2015.FTh4E.1 – volume: 136 start-page: 28 year: 2007 ident: ref_16 article-title: Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals publication-title: Sens. Actuators doi: 10.1016/j.sna.2007.02.012 – ident: ref_24 doi: 10.3390/s17020355 – ident: ref_69 – volume: 33 start-page: 2628 year: 2015 ident: ref_40 article-title: Distributed vibration sensing over 125 km with enhanced SNR using ϕ-OTDR over a URFL cavity publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2015.2396359 – ident: ref_56 doi: 10.1117/12.607175 – volume: 23 start-page: 2081 year: 2005 ident: ref_3 article-title: Distributed Fiber-Optic Intrusion Sensor System publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2005.849924 – ident: ref_22 doi: 10.1117/12.2192075 – ident: ref_75 doi: 10.1201/b16868 – volume: 9157 start-page: 9157D5-1 year: 2014 ident: ref_37 article-title: 175km Phase-sensitive OTDR with Hybrid Distributed Amplification publication-title: Proc. SPIE – volume: 34 start-page: 4445 year: 2016 ident: ref_23 article-title: Towards Prevention of Pipeline Integrity Threats using a Smart Fiber Optic Surveillance System publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2016.2542981 – volume: 22 start-page: 13804 year: 2014 ident: ref_35 article-title: Ultra-long high-sensitivity ϕ-OTDR for high spatial resolution intrusion detection of pipelines publication-title: Opt. Express doi: 10.1364/OE.22.013804 – ident: ref_70 doi: 10.21437/Interspeech.2004-337 – volume: 9157 start-page: 91576X-1 year: 2014 ident: ref_38 article-title: High sampling rate multi-pulse phase-sensitive OTDR employing frequency division multiplexing publication-title: Proc. SPIE – ident: ref_7 – ident: ref_53 – volume: 114 start-page: 7 year: 2004 ident: ref_14 article-title: Interrogation of multipoint optical fibre sensor signals based on artificial neural network pattern recognition techniques publication-title: Sens. Actuators doi: 10.1016/j.sna.2004.02.034 – volume: 39 start-page: 4313 year: 2014 ident: ref_34 article-title: Phase-sensitive optical time-domain reflectometry with Brillouin amplification publication-title: Opt. Lett. doi: 10.1364/OL.39.004313 – volume: 115 start-page: 293 year: 2004 ident: ref_15 article-title: A multipoint optical fibre sensor system for use in process water systems based on artificial neural network pattern recognition techniques publication-title: Sens. Actuators doi: 10.1016/j.sna.2004.03.068 – ident: ref_45 doi: 10.1109/ICIRT.2016.7588715 – volume: 15 start-page: 15179 year: 2015 ident: ref_21 article-title: Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction publication-title: Sensors doi: 10.3390/s150715179 – ident: ref_49 doi: 10.1109/CISE.2009.5365100 – ident: ref_50 doi: 10.1109/WGEC.2009.117 – volume: 17 start-page: 317 year: 2003 ident: ref_62 article-title: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features publication-title: Mech. Syst. Signal Process. doi: 10.1006/mssp.2001.1462 – volume: 102 start-page: 13 year: 2003 ident: ref_9 article-title: OTDR fiber-optical chemical sensor system for detection and location of hydrocarbon leakage publication-title: J. Hazard. Mater. doi: 10.1016/S0304-3894(03)00199-7 – volume: 9157 start-page: 9157O-1 year: 2014 ident: ref_19 article-title: A novel intrusion signal processing method for phase-sensitive optical time-domain reflectometry (ϕ-OTDR) publication-title: Proc. SPIE – volume: 19 start-page: 17 year: 2002 ident: ref_57 article-title: Detection, Classification and Tracking of Targets in Distributed Sensor Networks publication-title: IEEE Signal Process. Mag. doi: 10.1109/79.985674 – ident: ref_67 – volume: 7503 start-page: 75031O-1 year: 2009 ident: ref_30 article-title: Long-distance fiber-optic ψ-OTDR intrusion sensing system publication-title: Proc. SPIE – volume: 13 start-page: 8534 year: 2013 ident: ref_52 article-title: Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems publication-title: Sensors doi: 10.3390/s130708534 – volume: 47 start-page: 156 year: 2009 ident: ref_8 article-title: Experimental research on distributed fiber sensor for sliding damage monitoring publication-title: Opt. Lasers Eng. doi: 10.1016/j.optlaseng.2008.07.002 – volume: 9062 start-page: 906205-1 year: 2014 ident: ref_18 article-title: Vibration Pattern Recognition and Classification in OTDR Based Distributed Optical-Fiber Vibration Sensing System publication-title: Proc. SPIE – ident: ref_28 doi: 10.1117/12.484911 – ident: ref_59 doi: 10.1109/ICIECS.2009.5364602 – ident: ref_66 doi: 10.1093/oso/9780199214655.003.0001 – ident: ref_51 doi: 10.1109/ICIEA.2006.257218 – volume: 39 start-page: 5866 year: 2014 ident: ref_5 article-title: Ultra-long phase-sensitive OTDR with hybrid distributed amplification publication-title: Opt. Lett. doi: 10.1364/OL.39.005866 – volume: 31 start-page: 3631 year: 2013 ident: ref_44 article-title: Coherent noise reduction in high visibility phase sensitive optical time domain reflectometer for distributed sensing of ultrasonic waves publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2013.2286223 – ident: ref_2 – volume: 8 start-page: 2789 year: 2012 ident: ref_54 article-title: A Distance Segmented Classification Algorithm for Ground Moving Targets Based on Seismic-acoustic Sensor Arrays and Parallel Fuzzy Classifier publication-title: J. Comput. Inf. Syst. – volume: 91 start-page: 1163 year: 2003 ident: ref_60 article-title: Distributed Target Classification and Tracking in Sensor Networks publication-title: Proc. IEEE doi: 10.1109/JPROC.2003.814923 – volume: 24 start-page: 13121 year: 2016 ident: ref_72 article-title: Single-shot distributed temperature and strain tracking using direct detection phase-sensitive OTDR with chirped pulses publication-title: Opt. Express doi: 10.1364/OE.24.013121 – volume: 34 start-page: 301 year: 2003 ident: ref_13 article-title: A multi-point optical fibre sensor for condition monitoring in process water systems based on pattern recognition publication-title: Measurement doi: 10.1016/S0263-2241(03)00048-4 – ident: ref_48 doi: 10.1109/ICSPCom.2013.6719846 – volume: 15 start-page: 343 year: 1999 ident: ref_63 article-title: LANA: A Lane Extraction Algorithm that Uses Frequency Domain Features publication-title: IEEE Trans. Robot. Autom. doi: 10.1109/70.760356 – volume: E91-B start-page: 3544 year: 2008 ident: ref_47 article-title: A GMM-based Target Classification Scheme for a Node in Wireless Sensor Networks publication-title: IEICE Trans. Commun. doi: 10.1093/ietcom/e91-b.11.3544 – volume: 15 start-page: 21957 year: 2015 ident: ref_39 article-title: A Long Distance Phase-Sensitive Optical Time Domain Reflectometer with Simple Structure and High Locating Accuracy publication-title: Sensors doi: 10.3390/s150921957 – volume: 6770 start-page: 67700K-1 year: 2007 ident: ref_17 article-title: Intruder Signature Analysis from a Phase-sensitive Distributed Fiber-optic Perimeter Sensor publication-title: Proc. SPIE – ident: ref_43 – volume: 59 start-page: 321 year: 2008 ident: ref_68 article-title: A review of automated feature recognition with rule-based pattern recognition publication-title: Comput. Ind. doi: 10.1016/j.compind.2007.09.001 – volume: 9 start-page: 21 year: 1992 ident: ref_64 article-title: Linear and quadratic time-frequency signal representations publication-title: IEEE Signal Process. Mag. doi: 10.1109/79.127284 |
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| SubjectTerms | Artificial intelligence distributed acoustic sensing fiber optic systems Literature reviews Pattern recognition systems pipeline integrity threat monitoring review Surveillance ϕ-OTDR |
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| Title | Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review |
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