Abstract

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 $\text{eight}$ 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.

© 2016 IEEE

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