A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( -OTDR) technology for signal acquisition and pattern recognition strategies...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2017-02, Vol.17 (2), p.355-355
Main Authors: Tejedor, Javier, Macias-Guarasa, Javier, Martins, Hugo F, Piote, Daniel, Pastor-Graells, Juan, Martin-Lopez, Sonia, Corredera, Pedro, Gonzalez-Herraez, Miguel
Format: Article
Language:English
Subjects:
Classification
distributed acoustic sensing
feature-level contextual information
fiber optic systems
Gas pipelines
Health surveillance
Information dissemination
Multilayers
Natural gas
Optical fibers
Pattern classification
Pattern recognition
pipeline integrity threat monitoring
Proposals
Representations
Strategy
Surveillance systems
system combination
ϕ-OTDR
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Summary:This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes 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. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.
ISSN:1424-8220
1424-8220
DOI:10.3390/s17020355