Research on demodulation of FBGs sensor network based on PSO-SA algorithm

Qi, Yuefeng; Li, Cailing; Jiang, Peng; Jia, Cui; Liu, Yanyan; Zhang, Qi

doi:10.1016/j.ijleo.2018.03.088

Cited by 29 publications

(9 citation statements)

References 9 publications

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“…Several Bragg wavelength detection methods have been proposed in the introduction part, such as the differential evolution DE method [ 21 , 22 ], a tree-search dynamic multi-swarm particle swarm algorithm (TS-DMS-PSO) [ 23 ], particle swarm optimization-based simulated annealing (PSO-SA) [ 24 ], a search tree-based Least Square Support Regression [ 26 ], Genetic Algorithm, GA [ 20 ], distributed estimation algorithm (EDA) [ 25 ], and ELM [ 11 ]. Thus, in this section, we compare and contrast the performance of our proposed GRU model with previously proposed algorithms and machine learning techniques, as shown in Table 5 .…”

Section: Resultsmentioning

confidence: 99%

“…Figure 14 shows the detection results obtained by the GRU model when the number of FBG sensors is seven. As shown in the figure, the proposed model capable of detecting the Bragg wavelength of each FBG, even when the spectra of FBGs are partially overlapped (see strain steps 7,11,13,16,18,21,24,29,31,36,38,43,45,49,51, 56 and 58) and fully overlapped (see strain step 8, 12, 17, 23, 30, 37, 44, 50 and 57). Moreover, the average RMS errors of our proposed GRU model for the four different cases, such as 3-FBG, 5-FBG, 7-FBG, and 10-FBG, are summarized in Table 4.…”

Section: Case Three: 7-fbg Sensor Network Variable Strain Effect Detectionmentioning

confidence: 99%

“…Therefore, accurate determination of the FBG Bragg wavelength from the overlapping spectra is the primary task of the FBG sensor network. Recently, to boost the accuracy of Bragg wavelength measurements, several evolutionary algorithms such as genetic algorithms (GAs) [ 20 ], differential evolution (DE) algorithms [ 21 , 22 ], particle swarm (PSO) algorithms [ 23 , 24 ], and distributed estimation algorithms (EDA) [ 25 ] have been proposed. However, as the number of sensors increases, evolutionary algorithms typically need longer processing time to achieve better accuracy and have higher detection errors, which affect real-time monitoring of the Bragg wavelength measurement.…”

Section: Introductionmentioning

confidence: 99%

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Design Reliable Bus Structure Distributed Fiber Bragg Grating Sensor Network Using Gated Recurrent Unit Network

Dehnaw

Manie

Chen

et al. 2020

Sensors

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The focus of this paper was designing and demonstrating bus structure FBG sensor networks using intensity wavelength division multiplexing (IWDM) techniques and a gated recurrent unit (GRU) algorithm to increase the capability of multiplexing and the ability to detect Bragg wavelengths with greater accuracy. Several Fiber Bragg grating (FBG) sensors are coupled with power ratios of 90:10 and 80:10, respectively in the suggested experimental setup. We used the latest IWDM multiplexing technique for the proposed scheme, as the IWDM system increases the number of sensors and allows us to alleviate the limited operational region drawback of conventional wavelength division multiplexing (WDM). However, IWDM has a crosstalk problem that causes high-sensor signal measurement errors. Thus, we proposed the GRU model to overcome this crosstalk or overlapping problem by converting the spectral detection problem into a regression problem and considered the sequence of spectral features as input. By feeding this sequential spectrum dataset into the GRU model, we trained the GRU system until we achieved optimal efficiency. Consequently, the well-trained GRU model quickly and accurately identifies the Bragg wavelength of each FBG from the overlapping spectra. The Bragg wavelength detection performance of our proposed GRU model is tested or validated using different numbers of FBG sensors, such as 3-FBG, 5-FBG, 7-FBG, and 10-FBG, separately. As a result, the experiment result proves that the well-trained GRU model accurately identifies each FBG Bragg wavelength, and even the number of FBG sensors increase, as well as the spectra of FBGs, which are partially or fully overlapped. Therefore, to boost the detection efficiency, reliability, and to increase the multiplexing capabilities of FBG sensor networks, the proposed sensor system is better than the other previously proposed methods.

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Section: Resultsmentioning

confidence: 99%

Section: Case Three: 7-fbg Sensor Network Variable Strain Effect Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design Reliable Bus Structure Distributed Fiber Bragg Grating Sensor Network Using Gated Recurrent Unit Network

Dehnaw

Manie

Chen

et al. 2020

Sensors

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“…Software solutions are mainly focused on the coupling of the reflectance spectra of several FBG sensors into one sensing channel and processing the resulting signal for peaks discrimination [12,13]. Today, the scientific community is paying significant attention Sensors 2021, 21, 6188 2 of 9 to machine-learning (ML) algorithms, which have already shown good performance at various fundamental levels and practical applications [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, data-driven algorithms are capable of operating with large-scale high-dimensional data and finding hidden intrinsic features and dependencies. There have already been successive attempts to apply ML algorithms for interpretation the of overlapped reflectance spectra from sparse FBG sensors including: extreme learning machines [ 12 ], least squares support vector regression [ 16 ], convolutional neural networks [ 17 ], particle swarm optimization algorithms, long short-term memory algorithms [ 18 ] and others [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Highly Dense FBG Temperature Sensor Assisted with Deep Learning Algorithms

Kokhanovskiy

Shabalov

Dostovalov

et al. 2021

Sensors

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In this paper, we demonstrate the application of deep neural networks (DNNs) for processing the reflectance spectrum from a fiberoptic temperature sensor composed of densely inscribed fiber Bragg gratings (FBG). Such sensors are commonly avoided in practice since close arrangement of short FBGs results in distortion of the spectrum caused by mutual interference between gratings. In our work the temperature sensor contained 50 FBGs with the length of 0.95 mm, edge-to-edge distance of 0.05 mm and arranged in the 1500–1600 nm spectral range. Instead of solving the direct peak detection problem for distorted signal, we applied DNNs to predict temperature distribution from entire reflectance spectrum registered by the sensor. We propose an experimental calibration setup where the dense FBG sensor is located close to an array of sparse FBG sensors. The goal of DNNs is to predict the positions of the reflectance peaks of the reference sparse FBG sensors from the reflectance spectrum of the dense FBG sensor. We show that a convolution neural network is able to predict the positions of FBG reflectance peaks of sparse sensors with mean absolute error of 7.8 pm that is slightly higher than the hardware reused interrogator equal to 5 pm. We believe that dense FBG sensors assisted with DNNs have a high potential to increase spatial resolution and also extend the length of a fiber optical sensors.

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An Adaptive Tribal Topology for Particle Swarm Optimization

Brezinski

Ferens

2021

Transactions on Computational Science and Computational Intelligence

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Research on demodulation of FBGs sensor network based on PSO-SA algorithm

Cited by 29 publications

References 9 publications

Design Reliable Bus Structure Distributed Fiber Bragg Grating Sensor Network Using Gated Recurrent Unit Network

Design Reliable Bus Structure Distributed Fiber Bragg Grating Sensor Network Using Gated Recurrent Unit Network

Highly Dense FBG Temperature Sensor Assisted with Deep Learning Algorithms

An Adaptive Tribal Topology for Particle Swarm Optimization

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