Enhancing the Temperature-Measurement Efficiency in the Brillouin Optical Time-Domain Reflectometry (BOTDR) Fiber Sensor with the K-nearest Neighbor (K-NN) Algorithm

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Abstract

The authors propose to use the K-nearest neighbor (K-NN) algorithm to process the probe signals from the previously proposed Brillouin optical time-domain reflectometry (BOTDR) fiber sensor called the differential cross-spectrum BOTDR (DCS-BOTDR), which features a high spatial resolution. Widening of the Brillouin gain spectrum (BGS) when the pulse duration TL is set shorter than the phonon lifetime (~10 ns) is a key problem associated with the DCS-BOTDR. Although the spectrum distortion is reduced also, deterioration in the Brillouin frequency shift (BFS) resolution is observed, which is caused by the broadening of the Brillouin spectrum. On the other hand, while the TL duration exceeding the steady state of the Brillouin signal narrows the spectrum, sidelobes are produced within the steady-state range, which result in the deterioration of the BFS resolution. This limitation is observed only in the DCS-BOTDR, although it is capable of measuring with a high spatial resolution. The experimental data obtained for a fiber with a length of ~400 m and with optimized TL duration in the temperature range of 40–80°C were used in our model to extract the Brillouin temperature coefficient CT. Ideal BGSs were then constructed in the training phase by simulation with different linewidths of 50–70 MHz in order to train the K-NN model with due account for the linewidth variation caused by the difference in the conditions between the training and testing phases. The model was thereby made flexible for various fiber conditions. Experimental data for the sensing of a ~3.6-km-long fiber with TL = 60 ns were used in the testing phase to obtain the temperature distribution. By employing K-NN, the accuracy in determining the temperature for TL = 60 ns has been improved to approximately 2.77°C. Therefore, the K-NN model can be an excellent alternative tool for processing BGSs measured by the DCS-BOTDR and obtaining the temperature distribution along the fiber.

About the authors

Ahmed Sabri Kadhim Almoosa

Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM); Department of Electronic Technologies, Basra Technical Institute, Southern Technical University

Email: ahmedsabri1988@gmail.com
43300, Selangor, Bangi, Malaysia; 61001, Basra, Iraq

Mohd Saiful Dzulkefly Zan

Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM)

Email: yuri.al.konstantinov@ro.ru
43300, Selangor, Bangi, Malaysia

Mohd Faisal Ibrahim

Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM)

Email: yuri.al.konstantinov@ro.ru
43300, Selangor, Bangi, Malaysia

Norhana Arsad

Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM)

Email: yuri.al.konstantinov@ro.ru
43300, Selangor, Bangi, Malaysia

Mohd Hadri Hafiz Mokhtar

Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM)

Email: yuri.al.konstantinov@ro.ru
43300, Selangor, Bangi, Malaysia

Ahmad Ashrif A. Bakar

Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM)

Email: yuri.al.konstantinov@ro.ru
43300, Selangor, Bangi, Malaysia

Yu. A. Konstantinov

Perm Federal Research Center, Ural Branch, Russian Academy of Sciences (PFRC UB RAS)

Author for correspondence.
Email: yuri.al.konstantinov@ro.ru
614990, Perm, Russia

References

  1. Bai Q., Wang Q., Wang D., Wang Y., Gao Y., Zhang H., Zhang M., Jin B // Sensors. 2019.V. 19. P. 1862. https://doi.org/10.3390/s19081862
  2. Bao X., Chen L. // Sensors. 2012. V. 12. P. 8601. https://doi.org/10.3390/s120708601
  3. Hartog A.H. An introduction to distributed optical fibre sensors. CRC press. 2017. https://doi.org/10.1201/9781315119014
  4. Almoosa A.S.K., Hamzah A.E., Zan M.S.D., Ibrahim M.F., Arsad N., Elgaud M.M. // Opt. Fiber Technol. 2022. V. 70. P. 102860. https://doi.org/10.1016/j.yofte.2022.102860
  5. Zan M.S.D. et al. // Opt. Fiber Technol. 2022. V. 72. P. 102977. https://doi.org/10.1016/j.yofte.2022.102977
  6. Zan M. et al. // J. Physics: Conference Series 2021. V. 1892. P. 012034. https://doi.org/10.1088/1742-6596/1892/1/012034
  7. Koyamada Y., Sakairi Y., Takeuchi N., Adachi S. // IEEE Photonics Technol. Lett. 2007. V. 19. P. 1910. https://doi.org/10.1109/LPT.2007.908651
  8. Nishiguchi K.I., Li C.-H., Guzik A., Kishida K. // Sensors. 2014. V. 14. P. 4731. https://doi.org/10.3390/s140304731
  9. Horiguchi T., Masui Y., Zan M.S.D, // Sensors. 2019. V. 19. P. 1497. https://doi.org/10.3390/s19071497
  10. Shibata R., Kasahara H., Elias L.P., Horiguchi T. // IEICE Electron. Express. 2017. P. 14.20170267. https://doi.org/10.1587/elex.14.20170267
  11. Zan M.S.D., Masui Y., Horiguchi T. // In 2018 IEEE 7th International Conference on Photonics (ICP). 2018. P. 1. https://doi.org/10.1109/ICP.2018.8533208
  12. Bansal M., Goyal A., Choudhary A. // Decision Analytics J. 2022. V. 3. P.100071. https://doi.org/10.1016/j.dajour.2022.100071
  13. Chaplot N., Dhyani P., Rishi O. // Inter. J. Comput. Sci., Engin.& Technol. 2013. V. 1. P. 12. Corpus ID: 173168717
  14. Nordin N.D., Zan M.S.D., Abdullah F. // Opt. Fiber Technol. 2020. V. 58. P. 102298. https://doi.org/10.1016/j.yofte.2020.102298

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Copyright (c) 2023 Ahmed Sabri Kadhim Almoosa, Mohd Saiful Dzulkefly Zan, Mohd Faisal Ibrahim, Norhana Arsad, Mohd Hadri Hafiz Mokhtar, Ahmad Ashrif A. Bakar, Ю.А. Константинов