Improving the Brillouin frequency shift measurement resolution in the Brillouin optical time domain reflectometry (BOTDR) fiber sensor by artificial neural network (ANN)

Almoosa, Ahmed Sabri Kadhim; Hamzah, Abdulwahhab Essa; Zan, Mohd Saiful Dzulkefly; Ibrahim, Mohd Faisal; Arsad, Norhana; Elgaud, Mohamed M.

doi:10.1016/j.yofte.2022.102860

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…The obtained BFS distributions were tested via the trained ANN model. Results demonstrated that the ANN model was effective in improving the BFS resolution and can obtain a 20 cm spatial resolution at the same time [46].…”

Section: Single Probe Pulse Modulationmentioning

confidence: 99%

Probe pulse design in Brillouin optical time‐domain reflectometry

Wang

et al. 2022

IET Optoelectronics

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Brillouin optical time‐domain reflectometry (BOTDR) is a branch of distributed fibre‐optic sensors, and it can measure the strain and the temperature information, localised by the return time of the probe pulse, along the fibre based on the spontaneous Brillouin scattering process. Parameters of the BOTDR system, including the spatial resolution, the signal‐to‐noise ratio, the measurement speed, and the sensing range, have a mutually restrictive relationship. In order to improve the performance of the BOTDR system, researchers have focussed on improving the design of the probe pulse, for example, transforming the shape, the sequence, and the spectral properties of the pulse. This study summarises the recent progress in the design of detection pulses in BOTDR systems, and comprehensively demonstrates the improvement effects of various pulse modulation formats on the system performance.

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Section: Single Probe Pulse Modulationmentioning

confidence: 99%

Probe pulse design in Brillouin optical time‐domain reflectometry

Wang

et al. 2022

IET Optoelectronics

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“…In the extraction of BFS from single-peak BGS, feedforward neural networks 13 and convolutional neural networks 5 , 14 have been employed to improve the accuracy and efficiency. For double-peak BGS, the training of neural networks (NN) becomes more complex as more parameters need to be considered.…”

Section: Introductionmentioning

confidence: 99%

Extraction of double-peak Brillouin frequency shift based on ANN for nonuniform strain within BOTDR spatial resolution

Song,

Cui,

Jia

2024

Opt. Eng.

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.For practical engineering applications, Brillouin optical time domain reflectometry (BOTDR)’s double-peak Brillouin gain spectrum (BGS) can be measured when the strain is not uniform within spatial resolution. This double-peak BGS will affect the extraction accuracy of Brillouin frequency shift (BFS) and the following disaster monitoring. A detection method based on artificial neural network (ANN) is proposed for the double-peak BFS extraction. The ANN model is trained to deal with multi-parameters cases, including different frequency ranges, signal-to-noise-ratios, spectral widths, etc. The retraining of ANN model for different single-mode optical-fibers is not necessary. After training, the ANN model successfully detects the double-peak BFS from both the simulated and experimental data. The ANN model based BOTDR system is applied to the urban safety monitoring of underground pipe gallery. Under 5 m spatial resolution, 20 cm strained fiber can be detected, and the standard deviation of BFS can be as low as 1 MHz in experiment.

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“…Machine learning could learn signal features and regularity from large amounts of data and process them. In the field of distributed optical fiber sensing, several machine learning methods have been applied to improve the sensing performance in Brillouin optical time domain analysis (BOTDA) [ 16 , 17 , 18 ], Brillouin optical time domain reflectometer (BOTDR) [ 19 , 20 , 21 ], and phase-sensitive optical time domain reflectometer (Φ-OTDR) [ 22 , 23 , 24 , 25 ]. Yang G. et al proposed a convolutional neural network (CNN) model that consists of a one-dimensional denoising convolutional self-encoder and a one-dimensional residual attention network module; this model could extract both temperature and strain in a BOTDA system with better noise immunity and robustness under the conditions of wider temperature and strain ranges [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

Yin

Zhu

Liu

et al. 2023

Sensors

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We proposed an optical frequency domain reflectometry based on a multilayer perceptron. A classification multilayer perceptron was applied to train and grasp the fingerprint features of Rayleigh scattering spectrum in the optical fiber. The training set was constructed by moving the reference spectrum and adding the supplementary spectrum. Strain measurement was employed to verify the feasibility of the method. Compared with the traditional cross-correlation algorithm, the multilayer perceptron achieves a larger measurement range, better measurement accuracy, and is less time-consuming. To our knowledge, this is the first time that machine learning has been introduced into an optical frequency domain reflectometry system. Such thoughts and results would bring new knowledge and optimization to the optical frequency domain reflectometer system.

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Improving the Brillouin frequency shift measurement resolution in the Brillouin optical time domain reflectometry (BOTDR) fiber sensor by artificial neural network (ANN)

Cited by 13 publications

References 18 publications

Probe pulse design in Brillouin optical time‐domain reflectometry

Probe pulse design in Brillouin optical time‐domain reflectometry

Extraction of double-peak Brillouin frequency shift based on ANN for nonuniform strain within BOTDR spatial resolution

Optical Frequency Domain Reflectometry Based on Multilayer Perceptron

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