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A Contextual GMM-HMM Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection

AuthorsTejedor, J.; MacIas-Guarasa, J.; Martins, Hugo Fidalgo ; Martin-Lopez, S.; Gonzalez-Herraez, M
KeywordsAcoustic sensing
Distributed fiber sensing
Pattern recognition
Pipeline integrity
Phase-sensitive OTDR
Vibration sensing
Issue Date2-Apr-2019
PublisherOptical Society of America
CitationJournal of Lightwave Technology 37: 4514-4522 (2019)
AbstractThis 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 ( ¿¿ -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 and 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 modeling. The system runs in two different modes: first, machine+activity identification, which recognizes the activity being carried out by a certain machine, second, 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).
Publisher version (URL)http://dx.doi.org/10.1109/JLT.2019.2908816
Identifiersdoi: 10.1109/JLT.2019.2908816
issn: 0733-8724
Appears in Collections:(CFMAC-IO) Artículos
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