Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors
- 4 November 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Journal of Lightwave Technology
- Vol. 39 (5), 1537-1543
- https://doi.org/10.1109/jlt.2020.3035810
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 and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and universality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.Funding Information
- Sichuan Provincial Science and Technology plan (2020YJ0016, 2019YJ0228)
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