Undiscovered issues and solutions for direct detected ϕ-OTDR systems
Time and spatial domains in ϕ-OTDR perturbation detection and recognition for pipeline and border security applications in very long fiber under test (FUT) environments have not been properly analyzed so far. We propose in this pa- per several issues and the corresponding solutions that should be co...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/64549 |
| Acceso en línea: | http://hdl.handle.net/10017/64549 https://dx.doi.org/10.1016/j.yofte.2020.102346 |
| Access Level: | acceso abierto |
| Palabra clave: | Distributed acoustic sensing ϕ-OTDR Machine learning Detection Recognition Electrónica Electronics |
| Sumario: | Time and spatial domains in ϕ-OTDR perturbation detection and recognition for pipeline and border security applications in very long fiber under test (FUT) environments have not been properly analyzed so far. We propose in this pa- per several issues and the corresponding solutions that should be considered in both domains when developing those applications. Solutions are based on: (1) the importance of differential signals in direct detected ϕ-OTDR systems; (2) dealing with the useless data acquired in the time domain; (3) dealing with the zero and ghost energy points along the FUT which will undoubtedly lead to hiding the real perturbation and reducing the potential of finding the exact location of the perturbation, respectively. Experimental results show that: (1) setting the appropriate experimental conditions (i.e., increasing the intensity of the perturbation and sampling at the minimum sampling rate that satisfies the Nyquist criterion) is a must; (2) the Kendall correlation-based technique on differential signals allows obtaining more significant data traces both in time and spatial domains; (3) combining both methods obtains further gains; (4) S-Scan algorithm accurately provides the time instant of the real perturbation by increasing the probability of detecting zero energy points and avoiding ghost energy points in further processing. |
|---|