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 (ϕ-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.

This paper presents the first report on on-line and final blind field test results of a pipeline integrity threat surveillance system. The system integrates a machine+activity identification mode, and a threat detection mode. Two different pipeline sections were selected for the blind tests: One close to the sensor position, and the other 35 km away from it. Results of the machine+activity identification mode showed that about 46% of the times the machine, the activity or both were correctly identified. For the threat detection mode, 8 out of 10 threats were correctly detected, with 1 false alarm.

The preliminary results of a surveillance system set up for real time monitoring activities along a pipeline and analyzing for possible threats are presented. The system consists of a phi-OTDR based sensor used to monitor vibrations along an optical fiber combined with a pattern recognition system that classifies the recorded signals. The acoustic traces generated by the activities of different machines at various locations along a pipeline were recorded in the field. The signals, corresponding to machinery activities, were clearly distinguished from background noise. A threat classification rate of 68.11% with 55.55% false alarms was obtained.

A simple, in-line method for real-time full characterization (amplitude and phase) of propagation distortions arising because of group velocity dispersion and self-phase modulation on 10-20 Gbps transmitted NRZ optical signals is reported. It is based on phase reconstruction using optical ultrafast differentiation (PROUD), a linear and self-referenced technique. The flexibility of the technique is demonstrated by characterizing different data stream scenarios. Experimental results were modeled using conventional propagation equations, showing good agreement with the measured data. It is envisaged that the proposed method could be used in combination with DSP techniques for the estimation and compensation of propagation distortions in fiber links, not only in conventional IM/DD systems, but also in coherent systems with advanced modulation formats.