Multi-peak detection algorithm based on the Hilbert transform for optical FBG sensing

Liu, Fang; Tong, Xiaofeng; Zhang, Cui; Deng, Chengwei; Xiong, Qiao; Zheng, Zhiyuan; Wang, Pengfei

doi:10.1016/j.yofte.2018.06.003

Cited by 21 publications

(11 citation statements)

References 10 publications

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“…This is obviously significantly more difficult for gratings with wider linewidth resonances (LPGs, Ex-TFBGs) or when measurements are carried out with lower spectral resolution. In order to achieve such precision, peak fitting algorithms have been developed for the most common FBGs [190][191][192][193][194][195][196], based on model function fits, centroid calculation or Hilbert transform approaches. Of course, these techniques remain available for FBGs in multicore systems, as long as the cores are single mode.…”

Section: 6g Sensing In Multimodal Systems With Fiber Gratingsmentioning

confidence: 99%

Mode-division and spatial-division optical fiber sensors

et al. 2022

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The aim of this paper is to provide a comprehensive review of mode-division and spatial-division optical fiber sensors, mainly encompassing interferometers and advanced fiber gratings. Compared with their single-mode counterparts, which have a very mature field with many highly successful commercial applications, multimodal configurations have developed more recently with advances in fiber device fabrication and novel mode control devices. Multimodal fiber sensors considerably widen the range of possible sensing modalities and provide opportunities for increased accuracy and performance in conventional fiber sensing applications. Recent progress in these areas is attested by sharp increases in the number of publications and a rise in technology readiness level. In this paper, we first review the fundamental operating principles of such multimodal optical fiber sensors. We then report on the theoretical formalism and simulation procedures that allow for the prediction of the spectral changes and sensing response of these sensors. Finally, we discuss some recent cutting-edge applications, mainly in the physical and (bio)chemical fields. This paper provides both a step-by-step guide relevant for non-specialists entering in the field and a comprehensive review of advanced techniques for more skilled practitioners.

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Section: 6g Sensing In Multimodal Systems With Fiber Gratingsmentioning

confidence: 99%

Mode-division and spatial-division optical fiber sensors

et al. 2022

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“…have been reported [13]. For the multiple-peak detection of FBG based sensors the signal processing techniques such as Hilbert transform [14],segmentation based continuous wavelets transform [15], cross correlation with Hilbert transform [16], invariant moments retrieval method [17], centroid localization algorithm [18] have been demonstrated. In these methods, the drawback is that they fail to detect true peaks if the peaks are very narrow and weak.…”

Section: Introductionmentioning

confidence: 99%

Efficient detection of multiple FBG wavelength peaks using matched filtering technique

Kumar

Sengupta

2022

Opt Quant Electron

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We propose an efficient technique for FBG peak detection based on matched filtering technique. The matched filtering process is based on resonance point estimation between a standard reference spectral signal and a reflected spectrum of FBG. The desired peak wavelength and corresponding peak intensity are predicted by determining of the cross-correlation between the FBG signal and 3 rd derivative of the reference signal. The peak wavelength and intensity are found from the zero-crossing points of the crosscorrelation function. The Mexican-hat wavelet function is chosen as the reference spectral signal due to its narrow shape. The proposed algorithm can suitably be used for multiple peak detection when several FBGs are cascaded and if the FBG signal is weak and noisy.

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“…Through the analysis of the above recent references, it can be known that the models established using the hybrid algorithm have excellent prediction results. Montoya et al 4 and Li et al 10 used are FBGs, and Shan et al 5 used are distributed optical fiber sensors to study on the health monitoring of composite structure. The difference is that this article studied the load position identification of 2 3 2 optical fiber-composite structures made of fiber sensors based on intensity modulation.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that the proposed method can quantity monitoring the delamination damage of composites panel accurately, and the error of damage area is below 25%. Li et al 10 used the intelligent composite material impact location recognition technology based on the BP neural network system to obtain the time-domain signal response value of the FBG sensor to predict the impact position of the composite material. Simulation results showed that the BP neural network algorithm has the advantages of strong nonlinear approximation ability, high fault tolerance, and strong adaptive ability.…”

Section: Introductionmentioning

confidence: 99%

A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

Shen

Tian

2022

Measurement and Control

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Aiming at researching on health monitoring of composite materials, a static load position identification method for optical-fiber composite structures based on the Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithm is proposed. Based on the 2 × 2 optical fibers-composite structures, the PSO-BP algorithm is used to establish a nonlinear mapping between the fiber output intensity and the position. At first, a three-layer BP neural network is established. The number of the hidden layer is 30. And then the PSO algorithm is used to optimize the initial weights and thresholds of the BP neural network. Finally, a BP neural network is built using optimized initial weights and thresholds. A total of 515 sets of data samples are collected by the experimental system, of which 500 sets are used for training and 15 sets are used for the final model prediction. Simulation results show that the Mean Square Error (MSE) of the static load position prediction based on the PSO-BP algorithm is 0.0485. Compared with the position prediction model established by the BP neural network, Radial Basis Function (RBF) neural network and Support Vector Regression Machine (SVRM), the PSO-BP neural network model has a higher accuracy. The proposed method has an important application value for the research of health self-diagnosis of composite structures.

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Multi-peak detection algorithm based on the Hilbert transform for optical FBG sensing

Cited by 21 publications

References 10 publications

Mode-division and spatial-division optical fiber sensors

Mode-division and spatial-division optical fiber sensors

Efficient detection of multiple FBG wavelength peaks using matched filtering technique

A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

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