Search

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., European Commission, Comunidad de Madrid, Ministerio de Economía y Competitividad, Universidad de Alcalá

This paper presents an on-line augmented surveillance system that aims to real time monitoring of activities along a pipeline. The system is deployed in a fully realistic scenario and exposed to real activities carried out in unknown places at unknown times within a given test time interval (socalled blind field tests). We describe the system architecture that includes specific modules to deal with the fact that continuous on-line monitoring needs to be carried out, while addressing the need of limiting the false alarms at reasonable rates. To the best or our knowledge, this is the first published work in which a pipeline integrity threat detection system is deployed in a realistic scenario (using a fiber optic along an active gas pipeline) and is thoroughly and objectively evaluated in realistic blind conditions. The system integrates two operation modes: The machine+activity identification mode identifies the machine that is carrying out a certain activity along the pipeline, and the threat detection mode directly identifies if the activity along the pipeline is a threat or not. The blind field tests are carried out in two different pipeline sections: The first section corresponds to the case where the sensor is close to the sensed area, while the second one places the sensed area about 35 km far from the sensor. Results of the machine+activity identification mode showed an average machine+activity classification rate of 46:6%. For the threat detection mode, 8 out of 10 threats were correctly detected, with only 1 false alarm appearing in a 55:5-hour sensed period., European Commission, Ministerio de Economía y Competitividad, Comunidad de Madrid

25th International Conference on Optical Fiber Sensors, 2017, Jeju, Corea del Sur., 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., European Commission, Ministerio de Economía y Competitividad, Comunidad de Madrid

24th International Conference on Optical Fibre Sensors, Curitiba, Brazil, September 28, 2015., 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., European Commission, Ministerio de Economía y Competitividad, Comunidad de Madrid, Universidad de Alcalá, Ministerio de Ciencia e Innovación