Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor

Nur Dalilla Nordin,Mohd Saiful Dzulkefly Zan,Fairuz Abdullah

doi:10.3390/photonics7040079

Nur Dalilla Nordin, Mohd Saiful Dzulkefly Zan + Show 1 more

Open Access

https://doi.org/10.3390/photonics7040079

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Abstract

This paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). In this proof-of-concept experiment, the performance of each algorithm was investigated by pairing Brillouin gain spectrum (BGS) with its corresponding temperature reading in the training dataset. It was found that all of the ML algorithms have significantly reduced the signal processing time to be between 3.5 and 655 times faster than the conventional Lorentzian curve fitting (LCF) method. Furthermore, the temperature prediction accuracy and temperature measurement precision made by some algorithms were comparable, and some were even better than the conventional LCF method. The results obtained from the experiments would provide some general idea in deploying ML algorithm for characterizing the Brillouin-based fiber sensor signals.

Highlights

Research in the distributed Brillouin optical time domain analysis (BOTDA) fiber sensing technique has intensified over the past few decades after its first introduction in the late 1980s.This technique is capable to sense temperature or strain change with centimeter-scale spatial accuracy over a long distance [1,2,3,4,5,6]
The conventional Lorentzian curve fitting (LCF) method that was used in this paper was based on the Levenberg-Marquardt algorithm (LMA), and was calculated by using the Python lmfit module [40]
The results indicated that the root mean square error (RMSE) for all of the five machine learning (ML) models was lower than that of the conventional LCF method

Summary

Introduction

Research in the distributed Brillouin optical time domain analysis (BOTDA) fiber sensing technique has intensified over the past few decades after its first introduction in the late 1980s This technique is capable to sense temperature or strain change with centimeter-scale spatial accuracy over a long distance [1,2,3,4,5,6]. When the frequency difference between the pump and the probe coincides with the local Brillouin frequency shift (BFS), the acoustic wave is excited, resulting in the modulation of the refractive index of the fiber core. This process induces SBS, in which the energy is transferred from the pump to probe for Stokes shift case, and vice versa for Anti-Stokes shift case. The linear change in temperature or strain at any location along the fiber results in the linear change in the BFS, which makes BOTDA beneficial

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