Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Fiber Optic Sensors · Advanced Fiber Laser Technologies · Advanced Photonic Communication Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Technology, University CEU San Pablo, 28003 Madrid, Spain
Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain
FOCUS S.L., 28004 Madrid, Spain
Instituto de Óptica, CSIC, 28006 Madrid, Spain
Funding
European Research Council · Water JPI · WaterWorks2014 Cofunded Call · European Comission H2020 · Spanish Ministry of Economy and Competitiveness · Comunidad de Madrid · European Comission FP7 · Spanish Ministry of Science and Innovation
References (75)
Show source-provided references
1. European Gas Pipline Incident Data Group (EGIG) (2008). 7th EGIG-Report on Gas Pipeline Incidents (1970–2007), EGIG Secretariat. Technical Report.
2. Trust, P.S. (2017, July 01). Nationwide Data on Reported Incidents by Pipeline Type (Gas Transmission, Gas Distribution, Hazardous Liquid). Available online: http://pstrust.org/about-pipelines1/stats/accident/.
3. Distributed Fiber-Optic Intrusion Sensor System10.1109/JLT.2005.849924 ↗
4. Phase-sensitive Optical Time Domain Reflectometer Assisted by First-order Raman Amplification for Distributed Vibration Sensing Over >100 km10.1109/JLT.2014.2308354 ↗
5. Ultra-long phase-sensitive OTDR with hybrid distributed amplification10.1364/OL.39.005866 ↗
6. Kirkendall, C. (2007, January 25–29). Distributed Acoustic and Seismic Sensing. Proceedings of the Conference on Optical Fiber Communication and the National Fiber Optic Engineers Conference, Anaheim, CA, USA.10.1109/OFC.2007.4348619 ↗
7. Lumens, P. (2014). Fibre-Optic Sensing for Application in Oil and Gas Wells. [Ph.D. Thesis, Eindhoven University of Technology].
8. Experimental research on distributed fiber sensor for sliding damage monitoring10.1016/j.optlaseng.2008.07.002 ↗
9. OTDR fiber-optical chemical sensor system for detection and location of hydrocarbon leakage10.1016/S0304-3894(03)00199-7 ↗
10. Recent applications of fiber optic sensors to health monitoring in civil engineering10.1016/j.engstruct.2004.05.018 ↗
11. Hill, D. (2015, January 18–22). Distributed Acoustic Sensing (DAS): Theory and Applications. Proceedings of the Frontiers in Optic 2015, San Jose, CA, USA.10.1364/FIO.2015.FTh4E.1 ↗
12. An optical fibre distributed sensor based on pattern recognition10.1016/S0924-0136(02)00160-7 ↗
13. A multi-point optical fibre sensor for condition monitoring in process water systems based on pattern recognition10.1016/S0263-2241(03)00048-4 ↗
14. Interrogation of multipoint optical fibre sensor signals based on artificial neural network pattern recognition techniques10.1016/j.sna.2004.02.034 ↗
15. A multipoint optical fibre sensor system for use in process water systems based on artificial neural network pattern recognition techniques10.1016/j.sna.2004.03.068 ↗
16. Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals10.1016/j.sna.2007.02.012 ↗
17. Intruder Signature Analysis from a Phase-sensitive Distributed Fiber-optic Perimeter Sensor
18. Vibration Pattern Recognition and Classification in OTDR Based Distributed Optical-Fiber Vibration Sensing System
19. A novel intrusion signal processing method for phase-sensitive optical time-domain reflectometry (ϕ-OTDR)
20. A SVM-based pipeline leakage detection and pre-warning system10.1016/j.measurement.2009.12.022 ↗
21. Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction10.3390/s150715179 ↗
22. Martins, H., Piote, D., Tejedor, J., Macias, J., Pastor, J., Martin, S., Corredera, P., Smet, F.D., Postvoll, W., and Ahlen, C. (October, January 28). Early detection of pipeline integrity threats using a smart fiber-optic surveillance system: The PIT-STOP project. Proceedings of the International Conference on Optical Fibre Sensors 2015, Curitiba, Brazil.10.1117/12.2192075 ↗
23. Towards Prevention of Pipeline Integrity Threats using a Smart Fiber Optic Surveillance System10.1109/JLT.2016.2542981 ↗
24. Tejedor, J., Macias-Guarasa, J., Martins, H.F., Pastor-Graells, J., Martin-Lopez, S., Corredera, P., and Gonzalez-Herraez, M. (2017). A novel fiber optic based surveillance system for prevention of pipeline integrity threats. Sensors, 17.10.3390/s17020355 ↗
25. Martins, H., Piote, D., Tejedor, J., Macias, J., Pastor, J., Martin, S., Corredera, P., Smet, F.D., Postvoll, W., and Ahlen, C. (October, January 28). Towards detection of Pipeline Integrity Threats using a SmarT fiber-OPtic surveillance system: PIT-STOP project Blind Field Test results. Proceedings of the International Conference on Optical Fibre Sensors 2015, Curitiba, Brazil.10.1117/12.2192075 ↗
26. Field test of a fully distributed fiber optic intrusion detection system for long-distance security monitoring of national borderline
27. Separation and Determination of the Disturbing Signals in Phase-Sensitive Optical Time Domain Reflectometry (ϕ-OTDR)10.1109/JLT.2015.2421953 ↗
28. Choi, K.N., Juarez, J.C., and Taylor, H.F. (2003). Distributed fiber optic pressure/seismic sensor for low-cost monitoring of long perimeters. Proc. SPIE, 134–141.10.1117/12.484911 ↗
29. Field test of a distributed fiber-optic intrusion sensor system for long perimeters10.1364/AO.46.001968 ↗
30. Long-distance fiber-optic ψ-OTDR intrusion sensing system
31. Polarization discrimination in a phase-sensitive optical time-domain reflectometer intrusion-sensor system10.1364/OL.30.003284 ↗
32. Chao, P., Hui, Z., Bin, Y., Zhu, Z., and Xiahoan, S. (2012, January 28–31). Distributed optical-fiber vibration sensing system based on differential detection of differential coherent-OTDR. Proceedings of the IEEE Sensors 2012, Taipei, Taiwan.
33. Wavelet denoising method for improving detection performance of distributed vibration sensor10.1109/LPT.2011.2182643 ↗
34. Phase-sensitive optical time-domain reflectometry with Brillouin amplification10.1364/OL.39.004313 ↗
35. Ultra-long high-sensitivity ϕ-OTDR for high spatial resolution intrusion detection of pipelines10.1364/OE.22.013804 ↗
36. 124km Phase-sensitive OTDR with Brillouin Amplification
37. 175km Phase-sensitive OTDR with Hybrid Distributed Amplification
38. High sampling rate multi-pulse phase-sensitive OTDR employing frequency division multiplexing
39. A Long Distance Phase-Sensitive Optical Time Domain Reflectometer with Simple Structure and High Locating Accuracy10.3390/s150921957 ↗
40. Distributed vibration sensing over 125 km with enhanced SNR using ϕ-OTDR over a URFL cavity10.1109/JLT.2015.2396359 ↗
41. Continuous wavelet transform for nonstationary vibration detection with phase-OTDR10.1364/OE.20.020459 ↗
42. Enhancement of SNR and Spatial Resolution in ψ-OTDR System by Using Two-Dimensional Edge Detection Method10.1109/JLT.2013.2273553 ↗
43. Conway, C., and Mondanos, M. (2015, January 21–25). An introduction to fibre optic Intelligent Distributed Acoustic Sensing (iDAS) technology for power industry applications. Proceedings of the International Conference on Insulated Power Cables 2015, Paris, France.
44. Coherent noise reduction in high visibility phase sensitive optical time domain reflectometer for distributed sensing of ultrasonic waves10.1109/JLT.2013.2286223 ↗
45. Papp, A., Wiesmeyr, C., Litzenberger, M., Garn, H., and Kropatsch, W. (2016, January 23–25). A real-time algorithm for train position monitoring using optical time-domain reflectometry. Proceedings of the IEEE International Conference on Intelligent Rail Transportation 2016, Birmingham, UK.10.1109/ICIRT.2016.7588715 ↗
46. Cao, C., Fan, X.Y., Liu, Q.W., and He, Z.Y. (2015, January 19–23). Practical Pattern Recognition System for Distributed Optical Fiber Intrusion Monitoring System Based on Phase-Sensitive Coherent OTDR. Proceedings of the Asia Communications and Photonics Conference 2015, Hong Kong.10.1364/ACPC.2015.ASu2A.145 ↗
47. A GMM-based Target Classification Scheme for a Node in Wireless Sensor Networks10.1093/ietcom/e91-b.11.3544 ↗
48. Kandpa, M., Kakar, V.K., and Verma, G. (2013, January 12–14). Classification of Ground Vehicles Using Acoustic Signal Processing and Neural Network Classifier. Proceedings of the International Conference on Signal Processing and Communication 2013, Noida, India.10.1109/ICSPCom.2013.6719846 ↗
49. Li, J., Zhang, C., and Li, Z. (2009, January 11–13). Battlefield Target Identification Based on Improved Grid-search SVM Classifier. Proceedings of the International Conference on Computational Intelligence and Software Engineering 2009, Wuhan, China.10.1109/CISE.2009.5365100 ↗
50. Qi, X., Ji, J., Han, X., and Yuan, Z. (2009, January 14–17). An Approach of Passive Vehicle Type Recognition by Acoustic Signal Based on SVM. Proceedings of the International Conference on Genetic and Evolutionary Computing 2009, Guilin, China.10.1109/WGEC.2009.117 ↗
51. Shen, X., Wan, S.C., Huo, H., and Fang, T. (2006, January 24–26). An Improvement on Discrete Wavelet Transform-based Algorithm for Vehicle Classification in Wireless Sensor Networks. Proceedings of the IEEE Conference on Industrial Electronics and Applications 2006, Singapore.10.1109/ICIEA.2006.257218 ↗
52. Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems10.3390/s130708534 ↗
53. Wang, X., Qi, H., and Iyengar, S.S. (2002, January 8–11). Collaborative multi-modality target classification in distributed sensor networks. Proceedings of the International Conference on Information Fusion 2002, Annapolis, MD, USA.
54. A Distance Segmented Classification Algorithm for Ground Moving Targets Based on Seismic-acoustic Sensor Arrays and Parallel Fuzzy Classifier
55. Meesookho, C., Narayanan, S., and Raghavendra, C. (2002, January 6–6). Collaborative classification applications in sensor networks. Proceedings of the Sensor Array and Multichannel Signal Processing Workshop 2002, Rosslyn, VA, USA.
56. Necioglu, B.F., Christou, C.T., George, E.B., and Jacyna, G.M. (2005). Vehicle Acoustic Classification in Netted Sensor Systems Using Gaussian Mixture Models, The MITRE Corporation. Technical Report.10.1117/12.607175 ↗
57. Detection, Classification and Tracking of Targets in Distributed Sensor Networks10.1109/79.985674 ↗
58. Xing, H., Li, F., Xiao, H., Wang, Y., and Liu, Y. (2007). Ground Target Detection, Classification and Sensor Fusion in Distributed Fiber Seismic Sensor Network. Proc. SPIE, 1–10.10.1117/12.754927 ↗
59. Ji, J., Qi, X., Han, X., and Yuan, Z. (2009, January 19–20). An Approach of Automatic Vehicle Classification by Acoustic Wave based on PCA-RBF. Proceedings of the International Conference on Information Engineering and Computer Science 2009, Wuhan, China.10.1109/ICIECS.2009.5364602 ↗
60. Distributed Target Classification and Tracking in Sensor Networks10.1109/JPROC.2003.814923 ↗
61. Jobin, G., Leena, M., and Riyas, K.S. (2013, January 13–15). Vehicle Detection and Classification from Acoustic Signal Using ANN and KNN. Proceedings of the International Conference on Control Communication and Computing 2013, Thiruvananthapuram, India.
62. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features10.1006/mssp.2001.1462 ↗
63. LANA: A Lane Extraction Algorithm that Uses Frequency Domain Features10.1109/70.760356 ↗
64. Linear and quadratic time-frequency signal representations10.1109/79.127284 ↗
65. Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, The MIT Press.
66. Bishop, C.M., and Lasserre, J. (2007). Generative or Discriminative? Getting the Best of Both Worlds, Oxford University Press. Bayesian Statistics.10.1093/oso/9780199214655.003.0001 ↗
67. Ng, A.Y., and Jordan, M.I. (2001, January 3–8). On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. Proceedings of the Advances in Neural Information Processing Systems (NIPS) 2001, Vancouver, BC, Canada.
68. A review of automated feature recognition with rule-based pattern recognition10.1016/j.compind.2007.09.001 ↗
69. Kohavi, R. (1995, January 20–25). A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the International Joint Conference on Artificial Intelligence 1995, Montreal, QC, Canada.
70. Zhu, Q., Chen, B., Morgan, N., and Stolcke, A. (2004, January 4–8). On using MLP features in LVCSR. Proceedings of the Interspeech 2004, Jeju Island, Korea.10.21437/Interspeech.2004-337 ↗
71. FOCUS, S.L. (2017, July 01). Fiber Network Distributed Acoustic Sensor (FINDAS) Datasheet. Available online: http://www.focustech.eu/docs/FINDAS-MR-datasheet.pdf.
72. Single-shot distributed temperature and strain tracking using direct detection phase-sensitive OTDR with chirped pulses10.1364/OE.24.013121 ↗
73. Coherent ϕ-OTDR based on I/Q demodulation and homodyne detection10.1364/OE.24.000853 ↗
74. Real time dynamic strain monitoring of optical links using the backreflection of live PSK data10.1364/OE.24.022303 ↗
75. Stodden, V., Leisch, F., and Peng, R.D. (2014). Implementing Reproducible Research, CRC Press.10.1201/b16868 ↗