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

• Published in: Multimedia tools and applications Vol. 84; no. 12; pp. 11177 - 11201
• Main Authors: Tejedor, Javier, Macias-Guarasa, Javier, Martins, Hugo F., Martin-Lopez, Sonia, Gonzalez-Herraez, Miguel
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2025; Springer Nature B.V
• Subjects: Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Decision trees; False alarms; Feature extraction; Fiber optics; Gas pipelines; Markov chains; Multimedia Information Systems; Pattern classification; Pattern recognition; Pipelines; Probabilistic models; Special Purpose and Application-Based Systems; Surveillance systems; Threats; Distributed acoustic sensing; Pattern recognition; Hybrid approaches; Pipeline integrity; Phase-sensitive OTDR
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-19386-3

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 ( ϕ -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).