Improvement of Fiber Bragg Grating Wavelength Demodulation System by Cascading Generative Adversarial Network and Dense Neural Network

Abstract

A high-performance, low-cost demodulation system is essential for fiber-optic sensor-based measurement applications. This paper presents a demodulation system for FBG sensors based on a long-period fiber grating (LPG) driven by artificial intelligence techniques. The LPG is applied as an edge filter to convert the spectrum drift of the FBG sensor into transmitted intensity variation, which is subsequently fed to the proposed sensor demodulation network to provide high-precision wavelength interrogation. The sensor demodulation network consists of a generative adversarial network (GAN) for data augmentation and a dense neural network (DNN) for wavelength interrogation, the former addresses the drawback that traditional machine learning models rely on a large-scale dataset for satisfactory performance, while the latter is used to model the relationship between transmitted intensity and wavelength for demodulation. Experiments demonstrate that the proposed system has excellent performance and can achieve wavelength interrogation precision of ±3 pm. In addition, the effectiveness of the GAN is demonstrated. With a wide demodulation range, high performance, and low cost, the system can provide a new platform for fiber-optic sensor-based measurement applications.

Fiber-Optic System for Monitoring Pit Collapse Prevention

“…→ 0 [30,31], and calculating the volumetric density of the electromagnetic wave in the optic fiber in the form [32]:…”

“…It should be noted that the high sensitivity of Distributed Fiber-Optic Sensors (DFOS) is an advantage of such devices and a cause of measurement interference. In order to achieve high accuracy, such devices are used with machine learning systems [31]. Signal processing is most often carried out by constructing two-dimensional graphs; however, there are methods of identifying damage using a three-dimensional transformation [32].…”

Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding

“…A growing interest has been in integrating machine learning (ML) techniques into optical FBG sensing systems. For example, some researchers investigated using neural networks for peak tracking [20][21][22][23]. Additionally, other researchers recently investigated using machine learning algorithms with the optical FBG sensors for leakage detection, subway track vibration sensing, liquid level estimation, and temperature sensing [24][25][26][27].…”