Abstract
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.
Original language | English |
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Pages (from-to) | 340-351 |
Number of pages | 12 |
Journal | Measurement: Journal of the International Measurement Confederation |
Volume | 92 |
DOIs | |
Publication status | Published - 1 Oct 2016 |
Keywords
- Abrupt changes
- BOTDR
- Brillouin gain spectrum
- Outliers
- Smoothing
- Strain
- Time series
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering