LPG Interrogator Based on FBG Array and Artificial Neural Network

• Published in: IEEE sensors journal Vol. 20; no. 23; pp. 14187 - 14194
• Main Authors: Barino, Felipe Oliveira, Santos, Alexandre Bessa dos
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: artificial neural network; Artificial neural networks; Bragg gratings; Confidence intervals; Fiber gratings; Interrogation; Liquefied petroleum gas; Long-periodfiber gratings; LPG; machine learning; Multilayer perceptrons; Neural networks; Neurons; Optical fiber filters; optical fiber sensor; Optical fiber sensors; Optical fibers; Questioning; Sensor arrays; Sensors; Temperature measurement
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.3007957

This work introduces a new method for long-period fiber grating (LPG) sensors interrogation. This proposal uses a fiber Bragg grating (FBG) array to extract spectral information of the LPG sensor and an Artificial Neural Network to process this information. The information is processed to estimate the LPG resonant wavelength, without prior knowledge on the LPG spectrum. Therefore, the interrogator is LPG-insensitive, can be easily manufactured by optical fiber sensors laboratories at low-cost and is suitable for in-field applications. We demonstrated the filter array and Multilayer Perceptron (MLP) design, which are the proposed interrogator core. Furthermore, we analyzed the interrogation performance by the Mean Squared Error (MSE), the Mean Absolute Error (MAE), and the distribution of the residuals. The results showed our proposal can estimate the LPG resonant wavelength with 2.82 nm uncertainty, considering a 95% confidence interval, over 75 nm dynamic range for several LPGs, with different spectral characteristics. Moreover, our proposal can be easily tailored for different dynamic ranges and resolutions with proper adjustments on the FBG array and MLP.