Algorithm of FBG Spectrum Distortion Correction for Optical Spectra Analyzers with CCD Elements

Anfinogentov, Vladimir; Karimov, Kamil; Kuznetsov, Artem A.; Morozov, Oleg G.; Nureev, Ilnur I.; Sakhabutdinov, A. Zh.; Lipatnikov, K. A.; Hussein, Safaa; Ali, Mustafa H.

doi:10.3390/s21082817

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…The data obtained from the performance of the sensors are visualized and analyzed through the OSA in the range from 1547 to 1559 nm. The FBGs were separated by 3.9 nm at wavelengths to avoid overlap after independent measurements of strain and temperature (Anfinogentov et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Prototype of a sensor for simultaneous monitoring of water level and temperature of rivers in the Amazon using FBG

Oliveira

Sousa

et al. 2022

Opt Quant Electron

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This work proposes an optical fiber sensor capable of simultaneously determining the variation in the level and temperature of the waters of rivers in the Amazon using two in Fibers Bragg Grating (FBG) coupled to a metallic bellows structure, which was experimentally demonstrated in terms of the characterization of FBGs, where one of them is a temperature compensator. The system was simulated according to the Coupled Modes Theory (CMT) and the Transfer Matrix Method (TMM) and experimentally the sensitivity of the sensors was analyzed from the wavelength displacement measurements, simultaneously varying the deformation and temperature. The experimental results show a sensitivity of 9.2 pm/cm and water level measurements up to the limit of 3.95 m with a wavelength variation of 3.69 nm for the strain sensor. The proposed sensor is simple and has enormous potential to be used to monitor the level of rivers in the Amazon in areas at risk of flooding.

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Section: Methodsmentioning

confidence: 99%

Prototype of a sensor for simultaneous monitoring of water level and temperature of rivers in the Amazon using FBG

Oliveira

Sousa

et al. 2022

Opt Quant Electron

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“…This includes addressing the coupling of temperature and strain, electrolyte RI, and other influencing factors, such as the solid electrolyte layer versus the electrochemical interfacial layers. (2) Effective signal processing techniques can significantly reduce the impact of noise on optical signals, thereby enhancing the accuracy of battery condition measurements [142][143][144][145][146][147][148]. For instance, the Activation Function Dynamic Averaging (AFDA) algorithm outperforms the Frequency Domain Dynamic Averaging (FDDA) algorithm by processing signals eight times faster and improving the SNR by 3.7 dB independently, and by 10.8 dB when combined with MA-MD [143].…”

Section: Challenges and Outlooksmentioning

confidence: 99%

“…For instance, the Activation Function Dynamic Averaging (AFDA) algorithm outperforms the Frequency Domain Dynamic Averaging (FDDA) algorithm by processing signals eight times faster and improving the SNR by 3.7 dB independently, and by 10.8 dB when combined with MA-MD [143]. Additionally, a nonlinear distortion correction algorithm can rectify charge coupled device (CCD) measurement spectra, eliminating errors that may arise during various Time of INTegration (TINT) measurements [142]. Furthermore, a fiber grating filter is incorporated to enhance the overall signal processing capabilities [144].…”

Section: Challenges and Outlooksmentioning

confidence: 99%

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Yu,

Chen,

Deng

et al. 2024

Sensors

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Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 με), pressure (0.14 bar), and refractive index (6 × 10−5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/με, −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development.

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“…Our focus has been on a quasi-distributed system based on FBG sensing in optical fiber, where methods based on similar (but not the same) principles can be used [19]. The goal has been to avoid complex mathematical procedures, fingerprinting databases, or artificial intelligence-based techniques [19][20][21]. In [22,23], we reported on a denoising technique based on a digital sliding window.…”

Section: Introductionmentioning

confidence: 99%

Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing

Cibira,

Glesk,

Dubovan

et al. 2024

Sensors

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Many techniques have been studied for recovering information from shared media such as optical fiber that carries different types of communication, sensing, and data streaming. This article focuses on a simple method for retrieving the targeted information with the least necessary number of significant samples when using statistical population sampling. Here, the focus is on the statistical denoising and detection of the fiber Bragg grating (FBG) power spectra. The impact of the two-sided and one-sided sliding window technique is investigated. The size of the window is varied up to one-half of the symmetrical FBG power spectra bandwidth. Both, two- and one-sided small population sampling techniques were experimentally investigated. We found that the shorter sliding window delivered less processing latency, which would benefit real-time applications. The calculated detection thresholds were used for in-depth analysis of the data we obtained. It was found that the normality three-sigma rule does not need to be followed when a small population sampling is used. Experimental demonstrations and analyses also showed that novel denoising and statistical threshold detection do not depend on prior knowledge of the probability distribution functions that describe the FBG power spectra peaks and background noise. We have demonstrated that the detection thresholds’ adaptability strongly depends on the mean and standard deviation values of the small population sampling.

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Algorithm of FBG Spectrum Distortion Correction for Optical Spectra Analyzers with CCD Elements

Cited by 12 publications

References 26 publications

Prototype of a sensor for simultaneous monitoring of water level and temperature of rivers in the Amazon using FBG

Prototype of a sensor for simultaneous monitoring of water level and temperature of rivers in the Amazon using FBG

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing

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