Temperature drift compensation for FBG demodulation by utilizing LSTM neural networks

Jun Zhan; Wenjuan Sheng

doi:10.1117/12.2624924

27 March 2022 Temperature drift compensation for FBG demodulation by utilizing LSTM neural networks

Jun Zhan, Wenjuan Sheng

Proceedings Volume 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications; 1216972 (2022) https://doi.org/10.1117/12.2624924
Event: Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 2021, Kunming, China

Abstract

Piezo-electrical transducer (PZT) driven Fabry-Perot filter (F-P) filters are widely employed to implement a fast speed and high-resolution wavelength interrogation. Under a certain voltage, the transmission band of FFP can be tuned by changing the voltage applied on PZT. For the ideal design of the tunable filter actuation, the displacement–voltage relationship of PZT is assumed to be linear. However, temperature changes make the piezoelectric constant and dielectric constant of the material gradually change, and temperature drift leads to deterioration of the demodulation performance of tunable FabryPerot filters. Traditional calibration approaches are mainly based on F-P etalon and absorption lines of inert gas, but these approaches dramatically increase system cost and complexity. Considering the time series characteristics of temperature drift data, this paper proposes long and short-term memory (LSTM) neural network to compensate temperature drift of FP filters. During the cooling process of the F-P filter, the temperature and the drift of the reference fiber Bragg grating (FBG) are employed as the features of the LSTM model to characterize the wavelength drift of the sensing FBG. The experiment results show that after the wavelength is compensated with LSTM, the wavelength drift is reduced to 8.45 pm, while the compensated wavelength drift with the least square support vector machine (LSSVM) is 15.51 pm. Compared with LSSVM, LSTM is more suitable in long-term temperature-changing environments. Additionally, no additional hardware is required and the whole C band is covered in the proposed method.

Citation Download Citation

Jun Zhan and Wenjuan Sheng "Temperature drift compensation for FBG demodulation by utilizing LSTM neural networks", Proc. SPIE 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 1216972 (27 March 2022); https://doi.org/10.1117/12.2624924

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KEYWORDS

Fiber Bragg gratings

Demodulation

Neural networks

Data modeling

Ferroelectric materials

Calibration

Fabry–Perot interferometers

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