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Open AccessArticle

Fiber Bragg Grating Dynamic Calibration Based on Online Sequential Extreme Learning Machine

Department of Electronic and Communication Engineering, North China Electric Power University, No. 619 Yong Hua Street, Baoding 071003, China
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Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1840; https://doi.org/10.3390/s20071840 (registering DOI)
Received: 27 January 2020 / Revised: 22 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
(This article belongs to the Section Optical Sensors)
The fiber Bragg grating (FBG) sensor calibration process is critical for optimizing performance. Real-time dynamic calibration is essential to improve the measured accuracy of the sensor. In this paper, we present a dynamic calibration method for FBG sensor temperature measurement, utilizing the online sequential extreme learning machine (OS-ELM). During the measurement process, the calibration model is continuously updated instead of retrained, which can reduce tedious calculations and improve the predictive speed. Polynomial fitting, a back propagation (BP) network, and a radial basis function (RBF) network were compared, and the results showed the dynamic method not only had a better generalization performance but also had a faster learning process. The dynamic calibration enabled the real-time measured data of the FBG sensor to input calibration models as online learning samples continuously, and could solve the insufficient coverage problem of static calibration training samples, so as to improve the long-term stability, accuracy of prediction, and generalization ability of the FBG sensor.
Keywords: optical fiber sensors; fiber Bragg gratings; online sequential extreme learning machine; dynamic calibration optical fiber sensors; fiber Bragg gratings; online sequential extreme learning machine; dynamic calibration
MDPI and ACS Style

Shang, Q.; Qin, W. Fiber Bragg Grating Dynamic Calibration Based on Online Sequential Extreme Learning Machine. Sensors 2020, 20, 1840.

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