Toward Prevention of Pipeline Integrity Threats Using a Smart Fiber-Optic Surveillance System

This paper presents the first available report in the literature of a system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The system is based on phase-sensitive optical time-domain reflectometry technology for signal acquisition and pattern recognition strategies for threat identification. The system operates in two different modes: 1) machine+activity identification, which outputs the activity being carried out by a certain machine; and 2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. Different strategies dealing with position selection and normalization methods are presented and evaluated using a rigorous experimental procedure on realistic field data. Experiments are conducted with eight machine+activity pairs, which are further labeled as threat or nonthreat for the second mode of the system. The results obtained are promising given the complexity of the task and open the path to future improvements toward fully functional pipeline threat detection systems operating in real conditions.

[1]  Wei Zhang,et al.  EM algorithms of Gaussian mixture model and hidden Markov model , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[2]  Chris.  An introduction to fibre optic Intelligent Distributed Acoustic Sensing (iDAS) technology for power industry applications , 2015 .

[3]  Pedro Corredera,et al.  Distributed Vibration Sensing Over 125 km With Enhanced SNR Using Phi-OTDR Over a URFL Cavity , 2015, Journal of Lightwave Technology.

[4]  Xiaoyi Bao,et al.  Continuous wavelet transform for non-stationary vibration detection with phase-OTDR. , 2012, Optics express.

[5]  Yunjiang Rao,et al.  A novel intrusion signal processing method for phase-sensitive optical time-domain reflectometry (Φ-OTDR) , 2014, Other Conferences.

[6]  Fei Peng,et al.  Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines. , 2014, Optics express.

[7]  Xiaoyi Bao,et al.  Wavelet Denoising Method for Improving Detection Performance of Distributed Vibration Sensor , 2012, IEEE Photonics Technology Letters.

[8]  Jingchang Huang,et al.  Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems , 2013, Sensors.

[9]  Massimo L. Filograno,et al.  Coherent Noise Reduction in High Visibility Phase-Sensitive Optical Time Domain Reflectometer for Distributed Sensing of Ultrasonic Waves , 2013, Journal of Lightwave Technology.

[10]  S. Martin-Lopez,et al.  Early detection of pipeline integrity threats using a smart fiber optic surveillance system: the PIT-STOP project , 2015, International Conference on Optical Fibre Sensors.

[11]  Christi K. Madsen,et al.  Intruder signature analysis from a phase-sensitive distributed fiber-optic perimeter sensor , 2007, SPIE Optics East.

[12]  O. Frazão,et al.  Phase-sensitive Optical Time Domain Reflectometer Assisted by First-order Raman Amplification for Distributed Vibration Sensing Over >100 km , 2014, Journal of Lightwave Technology.

[13]  Jinghua Li,et al.  Battlefield Target Identification Based on Improved Grid-Search SVM Classifier , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[14]  J. Juarez,et al.  Distributed fiber-optic intrusion sensor system , 2005, Journal of Lightwave Technology.

[15]  Roberto Togneri,et al.  Using Gaussian mixture modeling in speech recognition , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Xiaohan Sun,et al.  Vibration pattern recognition and classification in OTDR based distributed optical-fiber vibration sensing system , 2014, Smart Structures.

[17]  Gaurav Verma,et al.  Classification of ground vehicles using acoustic signal processing and neural network classifier , 2013, 2013 INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION (ICSC).

[18]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[19]  Shrikanth Narayanan,et al.  Collaborative classification applications in sensor networks , 2002, Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002.

[20]  Z. N. Wang,et al.  Ultra-long phase-sensitive OTDR with hybrid distributed amplification. , 2014, Optics letters.

[21]  Fei Peng,et al.  Field test of a fully distributed fiber optic intrusion detection system for long-distance security monitoring of national borderline , 2014, Other Conferences.

[22]  Haim H. Permuter,et al.  A study of Gaussian mixture models of color and texture features for image classification and segmentation , 2006, Pattern Recognit..

[23]  Qian Sun,et al.  Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction , 2015, Sensors.

[24]  Tao Zhu,et al.  Enhancement of SNR and Spatial Resolution in $\varphi$-OTDR System by Using Two-Dimensional Edge Detection Method , 2013, Journal of Lightwave Technology.

[25]  Leena Mary,et al.  Vehicle detection and classification from acoustic signal using ANN and KNN , 2013, 2013 International Conference on Control Communication and Computing (ICCC).

[26]  H. A. David,et al.  The Paired t Test Under Artificial Pairing , 1997 .

[27]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[28]  Youngsoo Kim,et al.  An efficient scheme of target classification and information fusion in wireless sensor networks , 2009, Personal and Ubiquitous Computing.

[29]  Michael F. Cohen,et al.  Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).