An online two-stage adaptive algorithm for strain profile estimation from noisy and abruptly changing BOTDR data and application to underground mines

G. Soto, J. Fontbona, R. Cortez, L. Mujica

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

8 Citas (Scopus)

Resumen

Strain measurement using BOTDR (Brillouin Optical Time-Domain Reflectometry) is nowadays a standard tool for structural health monitoring. In this context, weak data quality and noise, usually owed to defective fiber installation, hinders discriminating actual level shifts from outliers and might entail a biased risk assessment. We propose a novel online adaptive algorithm for strain profile estimation in strain time series with abrupt and gradual changes and missing data. It relies on a convolution filter in Brillouin spectrum domain and a smoothing technique in time domain. In simulated data, the convolution filter is shown to reduce strain measurement uncertainty by up to 8 times the strain resolution. The two-stage method is illustrated with systematic outliers removal from real data of a Chilean copper mine and the improvement of the associated gain spectrum quality by up to 18 dB in SNR terms.

Idioma originalInglés
Páginas (desde-hasta)340-351
Número de páginas12
PublicaciónMeasurement: Journal of the International Measurement Confederation
Volumen92
DOI
EstadoPublicada - 1 oct 2016

Áreas temáticas de ASJC Scopus

  • Instrumental
  • Ingeniería eléctrica y electrónica
  • Matemáticas aplicadas
  • Estadística y probabilidad
  • Educación

Huella

Profundice en los temas de investigación de 'An online two-stage adaptive algorithm for strain profile estimation from noisy and abruptly changing BOTDR data and application to underground mines'. En conjunto forman una huella única.

Citar esto