research-article

Signal Analysis of Distributed Optic-Fiber Sensing Used for Oil and Gas Pipeline Monitoring

Authors:

Jing xin HeAuthors Info & Claims

SSPS '19: Proceedings of the 2019 International Symposium on Signal Processing Systems

Pages 21 - 25

https://doi.org/10.1145/3364908.3364918

Published: 20 September 2019 Publication History

Abstract

Distributed optic-fiber sensing technology based on coherent Rayleigh scattering can use optical fiber cable laying along with pipeline as vibration sensor, to give early-warning of the third-party threaten and even damage on oil and gas pipeline. First, an adaptive filtering is performed on the time-domain signals collected by all sensing units to make the preliminary judgment. Then, an advanced signal process method composed by convolutional neural network is proposed to extract the event areas and judge event categories. Field trial experiment results show that the system can effectively detect digging event with the recognition accuracy up to 97%.

References

[1]

YAO H Q, FENG W. 2010. Risk of Third Party's Damage and Control Measures for West-to-East Gas Transmission Pipeline [J]. Oil & Gas Storage and Transportation, 29(5):384--386.

[2]

WANG Q G, HE S B, DANG W Y. 2013. Study on performance evaluation of safety pre-warning technology on oil and gas pipeline [J]. Journal of Safety Science and Technology, 9(1):98--102.

[3]

SUN J, LI S, LIU K L, et al. 2016. Current status of security pre-warning technologies for oil and gas pipelines [J]. Oil & Gas Storage and Transportation, 35(9):1023--1026.

[4]

LI S. 2014. Current situation of oil/gas pipeline monitoring technologies J]. Oil & Gas Storage and Transportation, 33(2):129--134.

[5]

AN Y, JIN S J, FENG, et al. 2015. Optical Fiber Pipeline Security Pre-Warning System Based on Coherent Rayleigh Scattering [J]. Journal of Tianjin University, 2015(1).

[6]

ZHENG Y J, WANG Z D, YI S X, et al. 2008. Optical Fiber Pre-warning System for Safety of Oil Pipelines Based on OTDR [J]. CONTROL AND INSTRUMENTS IN CHEMICAL INDUSTRY, 35(2):45--49.

[7]

SONG Y B, HUANG J F. 2016. Distributed optical fiber sensing technology in the application of the oil and gas pipeline safety early warning system [J]. China's scientific and technological achievements, 17(10):55--57.

[8]

Widrow B, Mccool J M, Larimore M, et al. 1977. Stationary and Nonstationary Learning Characteristics of the LMS Adaptive Filter [M]// Aspects of Signal Processing.

[9]

Widrow B, Mccool J, Larimore M G, et al. 2005. Stationary and Nonstationary Learning Characteristics of the LMS Adaptive Filter [J]. Proceedings of the IEEE, 64(8):1151--1162.

[10]

Walach E, Widrow B. 1984. The least mean fourth (LMF) adaptive algorithm and its family [J]. IEEE Trans Information Theory, 30 (2):275--283.

Digital Library

[11]

Otsu N. 1975. A threshold selection method from gray-level histograms [J]. Automatica, 11(285--296): 23--27.

[12]

QI L N, ZHANG B, WANG Z K. 2006. Application of the OTSU Method in Image Processing [J]. RADIO ENGINEERING OF CHINA, 36 (7).

[13]

LI L L, DENG S X, DING X H. 2005. Binarization Algorithm Based on Image Partition Derived from Da-Jing Method [J]. CONTROL & AUTOMATION, (14):76--77.

[14]

Chua, L. O. 1998. CNN: A Paradigm for Complexity. CNN a paradigm for complexity /.

[15]

Chua, L. O., & Roska, T. 1993. The cnn paradigm. Circuits & Systems I Fundamental Theory & Applications IEEE Transactions on, 40(3), 147--156.

[16]

Roska, T., & Chua, L. O. 1992. The CNN universal machine. II. Programmability and applications. International Workshop on Cellular Neural Networks & Their Applications.

[17]

Zhao, Z. H., Yang, S. P., & Zeng-Qiang, M. A. 2010. License plate character recognition based on convolutional neural network lenet-5. Journal of System Simulation, 22(3), 638--641.

[18]

Yu, N., Jiao, P., & Zheng, Y. 2015. Handwritten digits recognition base on improved LeNet5. Control & Decision Conference.

[19]

El-Sawy, A., El-Bakry, H., & Loey, M. 2016. CNN for Handwritten Arabic Digits Recognition Based on LeNet-5. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. Springer, Cham.

[20]

Anil, R., Manjusha, K., Kumar, S. S., & Soman, K. P. 2015. Convolutional neural networks for the recognition of malayalam characters. International Journal of Computing Science & Mathematics, 5(2), 209--218.

Cited By

Wang CZhang RZhang HYang YMa YMeng J(2024)Distributed Fiber Optic Warning Identification Algorithm for Oil and Gas Pipelines based on the Inception-DVS ModelFlow Measurement and Instrumentation10.1016/j.flowmeasinst.2024.102802(102802)Online publication date: Dec-2024
https://doi.org/10.1016/j.flowmeasinst.2024.102802
Li JYao RZhang JZhang XRen MMa T(2023)Pipeline Threat Event Identification Based on GAF of Distributed Fiber Optic SignalsIEEE Sensors Journal10.1109/JSEN.2023.331593323:21(26796-26803)Online publication date: 1-Nov-2023
https://doi.org/10.1109/JSEN.2023.3315933
Liu KJin XJiang JXu TDing ZHuang YSun ZXue KLi SLiu T(2022)Interferometer-Based Distributed Optical Fiber Sensors in Long-Distance Vibration Detection: A ReviewIEEE Sensors Journal10.1109/JSEN.2022.321303622:22(21428-21444)Online publication date: 15-Nov-2022
https://doi.org/10.1109/JSEN.2022.3213036
Show More Cited By

Index Terms

Signal Analysis of Distributed Optic-Fiber Sensing Used for Oil and Gas Pipeline Monitoring
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

Recommendations

Fiber Optic Sensing Signal Monitoring System Based on FPGA
ICITEE '22: Proceedings of the 5th International Conference on Information Technologies and Electrical Engineering

At present, the phase monitoring method of optical fiber sensor signal cannot meet the practical application requirements of optical fiber communication. This paper proposes an optical fiber sensor signal monitoring system based on FPGA to solve the ...
Modelling and Simulation of Fiber Bragg Grating Characterization for Oil and Gas Sensing Applications
SIMS '14: Proceedings of the 2014 First International Conference on Systems Informatics, Modelling and Simulation

In this paper, modelling, simulation and characterization of optical fibre Bragg grating (FBG) for maximum reflectivity for oil and gas sensing applications are presented. The fibre grating length and the refractive index profile are key parameters for ...
Development and application of trenchless magnetic detector for oil and gas pipeline defect

Trenchless detection technology for oil and gas pipeline defect is of great significance to remedy the shortcomings of in-pipeline detection and pipeline direct assessment technology. Pipeline defect detection technology can be expanded and the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

SSPS '19: Proceedings of the 2019 International Symposium on Signal Processing Systems

September 2019

188 pages

ISBN:9781450362412

DOI:10.1145/3364908

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Beijing University of Posts and Telecommunications

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

SSPS 2019

SSPS 2019: 2019 International Symposium on Signal Processing Systems

September 20 - 22, 2019

Beijing, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
103
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)2

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang CZhang RZhang HYang YMa YMeng J(2024)Distributed Fiber Optic Warning Identification Algorithm for Oil and Gas Pipelines based on the Inception-DVS ModelFlow Measurement and Instrumentation10.1016/j.flowmeasinst.2024.102802(102802)Online publication date: Dec-2024
https://doi.org/10.1016/j.flowmeasinst.2024.102802
Li JYao RZhang JZhang XRen MMa T(2023)Pipeline Threat Event Identification Based on GAF of Distributed Fiber Optic SignalsIEEE Sensors Journal10.1109/JSEN.2023.331593323:21(26796-26803)Online publication date: 1-Nov-2023
https://doi.org/10.1109/JSEN.2023.3315933
Liu KJin XJiang JXu TDing ZHuang YSun ZXue KLi SLiu T(2022)Interferometer-Based Distributed Optical Fiber Sensors in Long-Distance Vibration Detection: A ReviewIEEE Sensors Journal10.1109/JSEN.2022.321303622:22(21428-21444)Online publication date: 15-Nov-2022
https://doi.org/10.1109/JSEN.2022.3213036
Park JYeom D(2022)A Conceptual Design for Underground Hydrogen Pipeline Monitoring SystemRecent Trends in Wave Mechanics and Vibrations10.1007/978-3-031-15758-5_79(775-782)Online publication date: 7-Oct-2022
https://doi.org/10.1007/978-3-031-15758-5_79
Garcia CCaiza GGarcia M(2022)Oil and Gas Upstream Sector: The Use of IEC-61499 and OPCHandbook of Smart Materials, Technologies, and Devices10.1007/978-3-030-84205-5_24(1051-1082)Online publication date: 10-Nov-2022
https://doi.org/10.1007/978-3-030-84205-5_24
Garcia CCaiza GGarcia M(2022)Oil and Gas Upstream Sector: The use of IEC-61499 and OPCHandbook of Smart Materials, Technologies, and Devices10.1007/978-3-030-58675-1_24-1(1-32)Online publication date: 26-Mar-2022
https://doi.org/10.1007/978-3-030-58675-1_24-1
Yang YZhang HLi Y(2021)Pipeline Safety Early Warning by Multifeature-Fusion CNN and LightGBM Analysis of Signals From Distributed Optical Fiber SensorsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2021.309251870(1-13)Online publication date: 2021
https://doi.org/10.1109/TIM.2021.3092518
Yang YZhang HLi Y(2021)Long-Distance Pipeline Safety Early Warning: A Distributed Optical Fiber Sensing Semi-Supervised Learning MethodIEEE Sensors Journal10.1109/JSEN.2021.308753721:17(19453-19461)Online publication date: 1-Sep-2021
https://doi.org/10.1109/JSEN.2021.3087537
Yang YLi YZhang H(2021)Pipeline Safety Early Warning Method for Distributed Signal using Bilinear CNN and LightGBMICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP39728.2021.9414544(4110-4114)Online publication date: 6-Jun-2021
https://doi.org/10.1109/ICASSP39728.2021.9414544

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten