Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach

We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber's multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model's uncertainties and select the features that contribute most to the model's performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature intimation addressing the well-known cross-sensitivity effects. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this paper, we report the first distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). The linear regression algorithm, a simple and well-interpretable machine learning and statistics method, is employed with the Brillouin frequency shifts and linewidths of the fiber's multipeak Brillouin spectrum as features. Machine learning concepts are utilized to estimate the model's uncertainties and select the most influential features to improve the regression algorithm's effectiveness. The model can also provide distributed temperature estimation simultaneously, addressing the cross-sensitivity effects.