Fiber-Optic Microstructure Sensors: A Review

Ran, Zengling; He, Xiaoqing; Rao, Yunjiang; Sun, Dong; Qin, Xiaolong; Zeng, Debiao; Chu, Wangwei; Li, Xiankun; Wei, Yabin

doi:10.1007/s13320-021-0632-7

Cited by 42 publications

(21 citation statements)

References 197 publications

(156 reference statements)

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“…is method has the advantages of simplicity, practicality, and low computational complexity. It is widely used in practice, but it often has the problem of low accuracy [4]. Yuan, and others divided the time-domain features of signals into short-time features of extracting signal envelope and long-time features of extracting signal features within a period of time, so as to analyze signals from multiple time scales and improve classi cation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical fiber intrusion signal recognition based on SCN (TSVD-SCN) based on truncated singular value decomposition (TSVD) is proposed in this paper. TSVD-SCN performs SVD decomposition on the hidden layer output of the network and sets a threshold to remove the smaller singular values, so as to reduce the number of conditions of the hidden layer output matrix and improve the network recognition rate. This paper uses the method of duty cycle, average amplitude difference function, and FFT to calculate the energy duty cycle for feature extraction and uses TSVD-SCN algorithm to classify and recognize different intrusion vibration feature vectors. The experimental results show that the root mean square errors of TSVD-SCN and SCN networks are significantly less than RVFL. After the hidden layer node L = 20 , the training error decline speed of RVFL tends to be gentle. When LRVFL = L max , the learning effect is the best, and RMSERVFL = 0.3 . With the continuous increase of L, the training error of SCN network and TSVD-SCN network will be reduced to very small, and the training error of TSVD-SCN network is also less than SCN. Conclusion. The accuracy of the algorithm model proposed in this paper is higher than that of the SCN model. It can accurately identify the types of optical fiber intrusion signals, which is of great significance to improve the classification accuracy of the SCN network in practical applications.

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Section: Introductionmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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“…For Point and quasi-distributed measurements, fiber-optic microstructure sensors including Fiber Bragg grating (FBG), long-period fiber grating, Fabry-Pérot interferometer sensors, Mach-Zehnder interferometer sensors, Michelson interferometer sensors, and Sagnac interferometer sensors are widely used [49].…”

Section: Photonic Sensors Based On Optical Fiber Technologymentioning

confidence: 99%

“…Acquiring an early indication of structural or mechanical problems allows operators to better plan for maintenance, possibly operate the machine in a de-rated condition rather than taking the unit offline, or in the case of an emergency, shut the machine down to avoid further damage. To build an accurate Structural Health Monitoring (SHM) system in the wind turbine, photonic sensors www.videleaf.com based on fiber technology installed on/in the blades are essential [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]. Even though several fiber optic transducer technologies are being employed to constitute the "nerve" part of the smart fiber optic sensor system, here, we will only briefly mention one based on FBGs (Figure 8).…”

Section: Quasidistributed Ofs For Structural Integrity Monitoring Of ...mentioning

confidence: 99%

Photonic Sensors

López‐Higuera¹

2022

Which Factors Contribute to Innovative Performance? A Case Study Applied to the Food and Beverage Industry

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This invited featured paper offers a Doctrinal Conception of sensing using Light (SuL) as an "umbrella" in which any sensing approach using Light Sciences and Technologies can be easily included. The key requirements of a sensing system will be quickly introduced by using a bottom-up methodology. Thanks to this, it will be possible to get a general conception of a sensor using Light techniques and know some related issues, such as its main constituted parts and types. The case in which smartness is conferred to the device is also considered. A quick "flight" over 10 significant cases using different principles, techniques, and technologies to detect diverse measurands in various sector applications is offered to illustrate this general concept. After reading this paper, any sensing approach using Light Sciences and Technologies may be easily included under the umbrella: sensing using Light or photonic sensors (PS).

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“…Due to its thin diameter, the OMFs provide other commendable features, such as outstanding mechanical flexibility, tight optical confinement and low optical loss [12]. There are several major types of OMFs sensors that have been developed recently, which include Mach-Zehnder OMFs interferometric sensors [13][14][15][16][17], OMFs Bragg/long period grating (FBG)-based sensors [18][19][20], OMFs coupler sensors [21][22][23][24], Sagnac OMFs interferometric sensors [25][26][27][28][29], and OMFs multimode interferometric sensors [30][31][32]. The various types of sensors, depending on their unique structures, possess different sensing performances, in terms of sensitivity and temperature stability.…”

Section: Introductionmentioning

confidence: 99%

A Novel Airborne Molecular Contaminants Sensor Based on Sagnac Microfiber Structure

Zhou

Xiang

You

et al. 2022

Sensors

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The impact of airborne molecular contaminants (AMCs) on the lifetime of fused silica UV optics in high power lasers (HPLs) is a critical issue. In this work, we demonstrated the on-line monitoring method of AMCs concentration based on the Sagnac microfiber structure. In the experiment, a Sagnac microfiber loop with mesoporous silica coating was fabricated by the microheater brushing technique and dip coating. The physical absorption of AMCs in the mesoporous coating results in modification of the surrounding refractive index (RI). By monitoring the spectral shift in the wavelength domain, the proposed structure can operate as an AMCs concentration sensor. The sensitivity of the AMCs sensor can achieve 0.11 nm (mg/m3). By evaluating the gas discharge characteristic of four different low volatilization greases in a coarse vacuum environment, we demonstrated the feasibility of the proposed sensors. The use of these sensors was shown to be very promising for meeting the requirements of detecting trace amounts of contaminants.

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Fiber-Optic Microstructure Sensors: A Review

Cited by 42 publications

References 197 publications

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Photonic Sensors

A Novel Airborne Molecular Contaminants Sensor Based on Sagnac Microfiber Structure

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