Numerical Modeling of Fcy OTDR Sensing Using a Refractive Index Perturbation Approach

Lu, Xin; Thomas, Peter J.

doi:10.1109/jlt.2019.2949624

Cited by 21 publications

(17 citation statements)

References 37 publications

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“…2. The pulse length is assumed to be equivalent to the spatial resolution of φ-OTDR, obtained as [14] Sampling point (Δd)…”

Section: A φ-Otdr Modelmentioning

confidence: 99%

“…As there is interference among the light backscattered from different inhomogeneities within the optical pulse, the obtained trace shows a stochastically varying amplitude. The contributions of the incident pulse electric field E in , reflected within pulse length d starting at position z, are summed up at the photodetector as [14] E(z) = e −αz+j2φ(z) M m=0 E in (m)r(z + m∆z)e j2φ(z+m∆z) ,…”

Section: A φ-Otdr Modelmentioning

confidence: 99%

“…Although we focus on road speed, other parameters can be estimated using the same framework. Synthetic φ-OTDR traces are generated by simulation using a validated model based on refractive index perturbations [14]. Vehicles are randomly generated to produce desired mean road speeds and car densities.…”

Section: Introductionmentioning

confidence: 99%

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Road-Speed Estimation Using Convolutional Neural Networks and Simulated ϕ-OTDR Traces

Colares¹,

Mello²

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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We investigate distributed fiber optic sensing and machine-learning-based image analysis for road-speed estimation. Synthetic phase-sensitive optical time-domain reflectometer (φ-OTDR) traces are generated by the simulation of random road features such as car density and speed. Consecutive φ-OTDR traces are stacked generating images that are submitted to a convolutional neural network (CNN) for classification. The evaluated CNN-based classifier exhibits high accuracy at sufficiently high car densities.

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“…2. The pulse length is assumed to be equivalent to the spatial resolution of φ-OTDR, obtained as [14] Sampling point (Δd)…”

Section: A φ-Otdr Modelmentioning

confidence: 99%

Section: A φ-Otdr Modelmentioning

confidence: 99%

See 1 more Smart Citation

Road-Speed Estimation Using Convolutional Neural Networks and Simulated ϕ-OTDR Traces

Colares¹,

Mello²

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…Subsequently in 2009, a DAS system was used to successfully measure an increase in oil production following a fracking intervention [ 26 ]. Since then, the DSs have formed the basis of a great deal of research, both in the development and understanding of the technologies [ 27 ], as well as their practical application. Due to their long-range and distributed monitoring capabilities, the distributed fibre sensors have been deployed for Vertical Seismic Profiling (VSP) [ 28 ], hydraulic fracture detection [ 29 ], early-warning systems for earthquake and seismic activity monitoring [ 30 ], traffic pattern analysis and monitoring [ 31 ], and in-well flow profiling that not only used in the laboratory or field trials but also in real-time operations [ 6 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Arief

Wiktorski

Thomas

2021

Sensors

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Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.

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“…Note that references [5,6] reveal that fibers with a standard acrylate coating suffer from humidity-induced strain. This can cause errors for other fiber measurements, particularly for distributed acoustic sensing [12] which demonstrates very high sensitivity. Both methods, however, have some drawbacks that prevent them from wide applications.…”

Section: Introductionmentioning

confidence: 99%

Distributed Humidity Sensing in Concrete Based on Polymer Optical Fiber

Hicke

Breithaupt

et al. 2021

Polymers

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We present a preliminary investigation on distributed humidity monitoring during the drying process of concrete based on an embedded polymer optical fiber (POF). The water dissipated into the POF changes several properties of the fiber such as refractive index, scattering coefficient and attenuation factor, which eventually alters the Rayleigh backscattered light. The optical time domain reflectometer (OTDR) technique is performed to acquire the backscattered signal at the wavelengths 650 nm and 500 nm, respectively. Experimental results show that the received signal increases at 650 nm while the fiber attenuation factor clearly increases at 500 nm, as the concrete dries out. In the hygroscopic range, the information retrieved from the signal change at 650 nm agrees well with the measurement result of the electrical humidity sensors also embedded in the concrete sample.

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Numerical Modeling of Fcy OTDR Sensing Using a Refractive Index Perturbation Approach

Cited by 21 publications

References 37 publications

Road-Speed Estimation Using Convolutional Neural Networks and Simulated ϕ-OTDR Traces

Road-Speed Estimation Using Convolutional Neural Networks and Simulated ϕ-OTDR Traces

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Distributed Humidity Sensing in Concrete Based on Polymer Optical Fiber

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