Abstract

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.

Signal processing using artificial neural network for BOTDA sensor system

Abul Kalam Azad, Liang Wang, Nan Guo, Hwa-Yaw Tam, and Chao Lu
Opt. Express 24(6) 6769-6782 (2016)

Integrated denoising and extraction of both temperature and strain based on a single CNN framework for a BOTDA sensing system

Guijiang Yang, Keyan Zeng, Liang Wang, Ming Tang, and Deming Liu
Opt. Express 30(19) 34453-34467 (2022)

Support vector machine assisted BOTDA utilizing combined Brillouin gain and phase information for enhanced sensing accuracy

Huan Wu, Liang Wang, Nan Guo, Chester Shu, and Chao Lu
Opt. Express 25(25) 31210-31220 (2017)

Distributed Brillouin frequency shift extraction via a convolutional neural network

Yiqing Chang, Hao Wu, Can Zhao, Li Shen, Songnian Fu, and Ming Tang
Photon. Res. 8(5) 690-697 (2020)

Deep neural networks assisted BOTDA for simultaneous temperature and strain measurement with enhanced accuracy

Biwei Wang, Liang Wang, Nan Guo, Zhiyong Zhao, Changyuan Yu, and Chao Lu
Opt. Express 27(3) 2530-2543 (2019)