Interrogation of a spectral profile division multiplexed FBG sensor network using a modified particle swarm optimization method

Abstract

This paper applies the concept of spectral profile division multiplexing to track each Bragg wavelength shift in a serially multiplexed fiber Bragg grating (FBG) network. Each sensor in the network is uniquely characterized by its own reflected spectrum shape, thus spectral overlapping is allowed in the wavelength domain. In contrast to the previous literature, spectral distortion caused by multiple reflections and spectral shadowing between FBG sensors, that occur in serial topology sensor networks, are considered in the identification algorithm. To detect the Bragg wavelength shift of each FBG, a nonlinear optimization function based on the output spectrum is constructed and a modified dynamic multi-swarm particle swarm optimizer is employed. The multiplexing approach is experimentally demonstrated on data from multiplexed sensor networks with up to four sensors. The wavelength prediction results show that the method can efficiently interrogate the multiplexed network in these overlapped situations. Specifically, the maximum error in a fully overlapped situation in the specific four sensor network demonstrated here was only 110 pm. A more general analysis of the prediction error and guidelines to optimize the sensor network are the subject of future work.

Mechanism of Spectral Distortion for Real-Time Crack Propagation on Aluminum Alloy Structure

“…Meanwhile, the FWHM displays an obvious correlation with crack length until a crack saturation length was reached. The spectrum bandwidth was also directly related to strain distribution and has been previously applied [24]. This is because there is a small offset between the center of the FBG and the intersection point between the FBG and the neutral line.…”

Young’s Modulus Measurement of Metal Wires Using FBG Sensor

“…zB  is the strain loaded on the FBG. It is obvious that the relative shift of Bragg wavelength is linearly related to the axial strain based on (3). When the axial stress is imposed to the wire by adding scales in the tray, the tensile elongation of the wire will transmit to the FBG.…”

“…The basic principle of FBG sensor is based on the fact that the Bragg wavelength shifts with the change of external environment parameters due to the thermo-optic effect and strain effect. FBG sensor exhibits all of the benefits associated with other optical fiber sensors, especially their ability to be multiplexed [2,3]. Many different FBG sensors have been developed for measuring strain [4], temperature [5,6], pressure [7], refractive index [8,9], curvature [10], pressure [11], and shock stress [12].…”

Wavelength demodulation technique for serial WDM fiber Bragg grating sensors based on light gated recurrent unit neural network and CCD interrogator

“…In order to address the issue of spectral overlap in FBG sensor networks that use WDM technology, various advanced wavelength detection methods have been proposed in recent years, for example, wavelength detection methods based on evolutionary algorithms, such as differential evolution (DE), 4 genetic algorithm (GA), 5 and particle swarm optimizer. 6 However, as the number of sensors in the network increases, these algorithms require a long computation time. Therefore, in order to improve the accuracy and reduce the detection time of spectral overlap detection, researchers have proposed various machine learning-based wavelength detection methods, such as extreme learning machine, 7 least squares support vector regression, 8 and multilayer perceptron (MLP).…”