Abstract

4 pags., 1 fig., 1 tab. 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. This work was mainly supported by three GERG partners (Fluxys, Statoil, and Gassco) under project PIT-STOP, and was also supported in part by: the European Research Council through project UFINE (Grant #307441); the EC Horizon 2020 program through the FINESSE project MSCA-ITN-ETN-722509; the DOMINO Water JPI project, under the WaterWorks2014 cofounded call by EC Horizon 2020 and Spanish MINECO; the Spanish MINECO through projects TEC2013-45265-R, TEC2015-71127-C2-2-R, and TIN2013-47630-C2-1-R; and the regional program SINFOTON-CM: S2013/MIT-2790. The work of HFM was supported by EU funding through the FP7 ITN ICONE program (Grant #608099). The work of JPG and SML was supported by the Spanish MINECO through FPI and \\Ramón y Cajal" contract, respectively. Peer Reviewed

Abstract

This paper presents a novel pipeline integrity surveillance system aimed to 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 ( $\\phi$ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level in a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM)-based pattern classification system and applies a system combination strategy for acoustic trace decision. System combination relies on majority voting of the decisions given by the individual contextual information sources and the number of states used for HMM modelling. The system runs in two different modes: (1) machine+activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed to detect threats no matter what the real activity being conducted is. In comparison with the previous systems based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information and the GMM-HMM approach improves the results for both machine+activity identification (7.6% of relative improvement with respect to the best published result in the literature on this task) and threat detection (26.6% of relative improvement in the false alarm rate with 2.1% relative reduction in the threat detection rate). Commission of the European Communities Joint Ministerio de Economía y Competitividad Comunidad de Madrid