Fast and high-accuracy temperature extraction of BOTDR sensor based on wavelet convolutional neural network

Bei Chen; Lianghao Su; Xiaozhi Liu; Zhaoyang Zhang; Muping Song; Yuehai Wang; Jianyi Yang

doi:10.1117/12.2619613

27 March 2022 Fast and high-accuracy temperature extraction of BOTDR sensor based on wavelet convolutional neural network

Bei Chen, Lianghao Su, Xiaozhi Liu, Zhaoyang Zhang, Muping Song, Yuehai Wang, Jianyi Yang

Author Affiliations +

Proceedings Volume 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications; 121690F (2022) https://doi.org/10.1117/12.2619613
Event: Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 2021, Kunming, China

Abstract

We propose an instantaneous temperature measurement method based on wavelet convolutional neural network (wavelet-CNN) to extract Brillouin frequency shift (BFS) in Brillouin optical time domain reflectometer (BOTDR) sensor and map the BFS to the temperature. Compared to Lorentzian curve fitting (LCF), both the simulation and experimental results show the wavelet-CNN has better accuracy and shorter processing time.

Citation Download Citation

Bei Chen, Lianghao Su, Xiaozhi Liu, Zhaoyang Zhang, Muping Song, Yuehai Wang, and Jianyi Yang "Fast and high-accuracy temperature extraction of BOTDR sensor based on wavelet convolutional neural network", Proc. SPIE 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 121690F (27 March 2022); https://doi.org/10.1117/12.2619613

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