A multi-position approach in a smart fiber-optic surveillance system for pipeline integrity threat detection
We present a new pipeline integrity surveillance system for long gas pipeline threat detection and classification. The system is based on distributed acoustic sensing with phase-sensitive optical time domain reflectometry (?-OTDR) and pattern recognition for event classification. The proposal incorp...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/64547 |
| Acceso en línea: | http://hdl.handle.net/10017/64547 https://dx.doi.org/10.3390/electronics10060712 |
| Access Level: | acceso abierto |
| Palabra clave: | Pipeline integrity threat monitoring Distributed acoustic sensing Fiber-optic ϕ-OTDR Pattern recognition Electrónica Electronics |
| Sumario: | We present a new pipeline integrity surveillance system for long gas pipeline threat detection and classification. The system is based on distributed acoustic sensing with phase-sensitive optical time domain reflectometry (?-OTDR) and pattern recognition for event classification. The proposal incorporates a multi-position approach in a Gaussian Mixture Model (GMM)-based pattern classification system which operates in a real-field scenario with a thorough experimental procedure. The objective is exploiting the availability of vibration-related data at positions nearby the one actually producing the main disturbance to improve the robustness of the trained models. The system integrates two classification tasks: (1) machine + activity identification, which identifies the machine that is working over the pipeline along with the activity being carried out, and (2) threat detection, which aims to detect suspicious threats for the pipeline integrity (independently of the activity being carried out). For the machine + activity identification mode, the multi-position approach for model training obtains better performance than the previously presented single-position approach for activities that show consistent behavior and high energy (between 6% and 11% absolute) with an overall increase of 3% absolute in the classification accuracy. For the threat detection mode, the proposed approach gets an 8% absolute reduction in the false alarm rate with an overall increase of 4.5% absolute in the classification accuracy. |
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