Title

Adaptive and precise peak detection algorithm for fibre Bragg grating using generative adversarial network

Journal title

Opto-Electronics Review

Yearbook

2022

Volume

30

Issue

4

Authors

Kumar, Sunil ; Sengupta, Somnath

Affiliation

Kumar, Sunil : Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India ; Sengupta, Somnath : Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

Keywords

fibre Bragg grating ; generative model ; discriminative model ; loss function

Divisions of PAS

Nauki Techniczne

Coverage

e144227

Publisher

Polish Academy of Sciences (under the auspices of the Committee on Electronics and Telecommunication) and Association of Polish Electrical Engineers in cooperation with Military University of Technology

Bibliography

1. Chen, G. Y. & Brambilla, G. Optical Microfiber Physical Sensors. in Optical Fiber Sensors: Advanced Techniques and Applications (ed. Rajan, G.) chapter 8 (CRC Press, 2017).
2. Fiber optic bio and chemical sensors. in Fiber optic sensors (eds. Yin, Sh., Ruffin, P. B. & Yu, F. T. S.) 435–457 (CRC Press, 2008).
3. Ma, Z. & Chen, X. Fiber Bragg gratings sensors for aircraft wing shape measurement: Recent applications and technical analysis. Sensors 19, 55 (2018). https://doi.org/10.3390/s19010055
4. Jinachandran, S. et al. Fabrication and characterization of a magnetized metal-encapsulated FBG sensor for structural health monitoring. IEEE Sensor J. 18, 8739–8746 (2018). https://doi.org/10.1109/JSEN.2018.2866803
5. Gautam, A., Kumar, A. & Priya, V. Microseismic wave detection in coal mines using differential optical power measurement. Opt. Eng. 58 056111 (2019). https://doi.org/10.1117/1.OE.58.5.056111
6. Kinjalk, K., Kumar, A. & Gautam, A. High-resolution FBG-based inclination sensor using eigen decomposition of reflection spectrum. IEEE Trans. Instrum. Meas. 69, 9124–9131 (2020). https://doi.org/10.1109/TIM.2020.2999116
7. Vickers, N. J. Animal communication: when I’m calling you, will you answer too. Curr. Biol. 27, R713–R715 (2017). https://doi.org/10.1016/j.cub.2017.05.064
8. An, Y., Wang, X., Qu, Zh., Liao, T. & Nan, Zh. Fiber Bragg grating temperature calibration based on BP neural network. Optik 172, 753–759 (2018). https://doi.org/10.1016/j.ijleo.2018.07.064
9. Chen, Z.-J. et al. Optimization and comparison of the peak-detection algorithms for the reflection spectrum of fiber Bragg grating. Acta Photon. Sin. 44, 1112001 (2015). [in Chinese]
10. Trita, A. et al. Simultaneous interrogation of multiple fiber Bragg grating sensors using an arrayed Waveguide grating filter fabricated in SOI platform. IEEE Photon. J. 7, 1–11 (2015). https://doi.org/10.1109/JPHOT.2015.2499546
11. Junfeng, J. et al. Distortion-tolerated high speed FBG demodulation method using temporal response of high-gain photodetector. Opt. Fiber Technol. 45, 399–404 (2018). https://doi.org/10.1016/j.yofte.2018.08.019
12. Kumar, S. et al. Efficient detection of multiple FBG wavelength peaks using matched filtering technique. Opt. Quantum Electron. 54, 1–14 (2022). https://doi.org/10.1007/s11082-021-03460-3
13. Liu, F. et al. Multi-peak detection algorithm based on the Hilbert transform for optical FBG sensing. Opt. Fiber Technol. 45, 47–52 (2018). https://doi.org/10.1016/j.yofte.2018.06.003
14. Theodosiou, A. et al. Accurate and fast demodulation algorithm for multipeak FBG reflection spectra using a combination of cross-correlation and Hilbert transform. J. Light. Technol. 35, 3956–3962 (2017). https://doi.org/10.1109/JLT.2017.2723945
15. Chen, Y., Yang, K. & Liu, H.-L. Self-adaptive multi-peak detection algorithm for FBG sensing signal. IEEE Sensors J. 16 2658–2665 (2016). https://doi.org/10.1109/JSEN.2016.2516038
16. Guo, Y., Yu, C., Yi, N. & Wu, H. Accurate demodulation algorithm for multi-peak FBG sensor based on invariant moments retrieval. Opt. Fiber Technol. 54, 102129 (2020). https://doi.org/10.1016/j.yofte.2019.102129
17. Li, Hong, et al. Recognition and classification of FBG reflection spectrum under non-uniform field based on support vector machine. Opt. Fiber Technol. 60, 102371 (2020). https://doi.org/10.1016/j.yofte.2020.102371
18. Nascimento, K. P., Frizera-Neto, A., Marques, C. & Leal-Junior, A. G. Machine learning techniques for liquid level estimation using FBG temperature sensor array. Opt. Fiber Technol. 65, 102612 (2021). https://doi.org/10.1016/j.yofte.2021.102612
19. Jiang, H., Cheng, J. & Liu, T. Wavelength detection in spectrally overlapped FBG sensor network using extreme learning machine. IEEE Photon. Technol. Lett. 26, 2031–2034 (2014). https://doi.org/10.1109/LPT.2014.2345062
20. Leal-Junior, A. G.A machine learning approach for simultaneous measurement of magnetic field position and intensity with fiber Bragg grating and magnetorheological fluid. Opt. Fiber Technol. 56, 102184 (2020). https://doi.org/10.1016/j.yofte.2020.102184
21. Ee, Y.-J. et al. Lithium-ion battery state of charge (SoC) estimation with non-electrical parameter using uniform fiber Bragg grating (FBG). J. Energy Storage 40, 102704 (2021). https://doi.org/10.1016/j.est.2021.102704
22. Kokhanovskiy, A., Shabalov, N., Dostovalov, A. & Wolf, A. Highly dense FBG temperature sensor assisted with deep learning algorithms. Sensors 21, 6188 (2021). https://doi.org/10.3390/s21186188
23. Cao, Z., Zhang, S., Liu, Z. & Li, Z. Spectral demodulation of fiber Bragg grating sensor based on deep convolutional neural networks. J. Light Technol. 40, 4429–4435 (2022). https://doi.org/10.1109/JLT.2022.3155253
24. Manie, Y. Ch. et al. Using a machine learning algorithm integrated with data de-noising techniques to optimize the multipoint sensor network. Sensors 20, 1070, (2020). https://doi.org/10.3390/s20041070

Date

27.12.2022

Type

Article

Identifier

DOI: 10.24425/opelre.2022.144227