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A hybrid cascade-parallel discriminative-generative model for pipeline integrity threat detection in a smart fiber optic surveillance system

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Abstract

This paper presents an advanced system for the continuous monitoring of potential threats in a long gas pipeline. For signal acquisition, phase-sensitive optical time domain reflectometry (\(\phi \)-OTDR) technology is employed. Then, pattern recognition strategies are incorporated, which are aimed at identifying threats. To do so, the system integrates a random forest-based approach on top of a multiple-layer perceptron (MLP)-based discriminative approach for feature extraction within a parallel Gaussian Mixture Model (GMM)-Hidden Markov Model (HMM) for pattern classification in a hybrid approach. Subsequently, a system combination strategy, which makes use of the decisions carried out by this hybrid approach, is also presented. This strategy is based on the so-called majority voting technique, which makes use of the output of the classification step from the different feature extraction strategies and the different number of states in the GMM-HMM-based classification. The system is tested on two tasks: (1) Identification of machine and activity, and (2) detection of threats for the pipeline. Compared with our previous system, the results of this advanced system show that the hybrid feature extraction and pattern classification achieve statistically significant improvements for both tasks (i.e., 5% of relative improvement for the machine and activity identification task, 1% of relative improvement in the threat detection rate, and 15% of relative improvement in the false alarm rate for the threat detection task).

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Acknowledgements

This work was partially supported by the Ministry of Science, Innovation and Universities of Spain (grant number RTI2018-095324-B-I00). This work was also funded by the Spanish Ministry of Economy and Competitiveness with projects ARTEMISA (TIN2016-80939-R) and HEIMDAL-UAH (TIN2016-75982-C2-1-R), by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR program, with projects PSI (PLEC2021-007875), ATHENA (PID2020-115995RB-I00) and EYEFUL (PID2020-113118RB-C31), and by CAM and UAH under projects ARGOS+ (PIUAH21/IA-016) and CONDORDIA (CM/JIN/2021-015). The authors gratefully acknowledge the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Fisica Corpuscular, IFIC (CSIC-UV). The authors thank Sira E. Palazuelos-Cagigas for her participation in the writing and technical editing of the manuscript.

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Authors and Affiliations

  1. Department of Information Technology, Institute of Technology, Universidad San Pablo-CEU, CEU universities, Urbanización Montepríncipe, Boadilla del Monte, 28668, Spain

    Javier Tejedor

  2. Universidad de Alcalá, Department of Electronics, Campus Universitario. Ctra. Madrid-Barcelona, Km. 33,600, Alcalá de Henares, 28801, Spain

    Javier Macias-Guarasa, Sonia Martin-Lopez & Miguel Gonzalez-Herraez

  3. Instituto de Óptica Daza de Valdés, Consejo Superior de Investigaciones Científicas (CISC), Calle Serrano, 121, Madrid, 28006, Spain

    Hugo F. Martins

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  1. Javier Tejedor
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Tejedor, J., Macias-Guarasa, J., Martins, H.F. et al. A hybrid cascade-parallel discriminative-generative model for pipeline integrity threat detection in a smart fiber optic surveillance system. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19386-3

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