Sawtooth Fiber MZ Vector Bending Sensor Available for Multi Parameter Measurement

Wei, Yong; Jiang, Tianci; Liu, Chunlan; Zhao, Xiao‐Ling; Wang, Rui; Shi, Chen; Liu, Chunbiao

doi:10.1109/jlt.2022.3186345

Cited by 20 publications

(5 citation statements)

References 24 publications

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“…The conventional methods for enhancing the performance of MMF sensors can be classified into three categories: new structure, new specialty fibers, and new compensation methods. The sensors reviewed in section 2 are selected to showcase these three approaches, which include unique structures like balloon-like structures [45,68,82], S-tapered structures [50], Kretschmann structures [67], and sawtooth structures [89]; notable specialty fibers such as HCF [66,85], SPF-TCPCF [46], 7-CF [87], and 4-CF [88]; and compensation methods, e.g. using FBG or LPG [41,43,44,47,76] to discriminate the cross-sensitivity and thin-film coating [67,79,[83][84][85]90] to improve the sensing performance.…”

Section: Discussion and Prospectionmentioning

confidence: 99%

“…Last year, a sawtooth fiber structure-based MZI was proposed by Wei et al [89] for curvature sensing and bending direction recognition (figure 5(d)), which was also possible to be used for multi-parameter sensing. The cladding of an SMF was removed at equal intervals by a CO 2 laser to fabricate the sawtooth structure, and two MMFs were spliced at both ends of this SMF to form an MSM hetero-core structure.…”

Section: Curvature Sensorsmentioning

confidence: 99%

“…Schematic diagrams of (a) MZI with HCF covered with a PDMS polymer [86], (b) SMF-MMF-7-CF-MMF-SMF structure (© 2020 IEEE. Reprinted, with permission, from [87]), (c) MZI based on a 4-CF (reproduced with permission from[88]), (d) sawtooth fiber structure-based MZI[89], (e) SPR sensor based on a V-groove fiber[90], (f) SIMMF-GIMMF-SIMMF structure (reproduced from[91]. CC BY 4.0), (g) MZI based on inclined-plane-polished HCF filled with PDMS[92], (h) enhanced MMF core mismatch structure[93].…”

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confidence: 99%

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Multimode optical fiber sensors: from conventional to machine learning-assisted

Wang,

Mizuno,

Dong

et al. 2023

Meas. Sci. Technol.

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Multimode fiber (MMF) sensors have been extensively developed and utilized in various sensing applications for decades. Traditionally, the performance of MMF sensors was improved by conventional methods that focused on structural design and specialty fibers. However, in recent years, the blossom of machine learning techniques has opened up new avenues for enhancing the performance of MMF sensors. Unlike conventional methods, machine learning techniques do not require complex structures or rare specialty fibers, which reduces fabrication difficulties and lowers costs. In this review, we provide an overview of the latest developments in MMF sensors, ranging from conventional methods to those assisted by machine learning. This article begins by categorizing MMF sensors based on their sensing applications, including temperature and strain sensors, displacement sensors, refractive index sensors, curvature sensors, bio/chemical sensors, and other sensors. Their distinct sensor structures and sensing properties are thoroughly reviewed. Subsequently, the machine learning-assisted MMF sensors that have been recently reported are analyzed and categorized into two groups: learning the specklegrams and learning the spectra. The review provides a comprehensive discussion and outlook on MMF sensors, concluding that they are expected to be utilized in a wide range of applications.

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Section: Discussion and Prospectionmentioning

confidence: 99%

Section: Curvature Sensorsmentioning

confidence: 99%

mentioning

confidence: 99%

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Multimode optical fiber sensors: from conventional to machine learning-assisted

Wang,

Mizuno,

Dong

et al. 2023

Meas. Sci. Technol.

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“…First of all, the proposed learningenhanced fiber specklegram bending sensor can realize bending recognition without designing and manufacturing complex sensing structures. In the previously reported work, it is often necessary to design and fabricate long-period fiber gratings [30][31][32] or fiber interferometers [33] with complex structures to realize bending sensing, which will destroy the robustness of the fiber and require additional experimental effort and time costs. In this work, a multimode optical fiber without any processing can be used to achieve high-precision bending sensing, which undoubtedly improves the practicability and application range of the scheme.…”

Section: The Contribution Rate Of Principal Components Under Differen...mentioning

confidence: 99%

Bending recognition based on learning enhanced fiber specklegram sensor

Gao,

Zhang,

Zhang

et al. 2023

Meas. Sci. Technol.

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In this paper, we propose and demonstrate a deep learning-enhanced fiber specklegram sensor for bending recognition. A segment of multimode fiber is used to sense bending, and tiny bending change leads to significant variations in the speckle pattern generated at the distal end of the fiber. Principal component analysis is utilized to optimize the collected samples and remove noise and redundant information by mining internal features, which makes the mapping relationship between speckle image and corresponding curvature clearer and is conducive to reducing computational complexity. BP neural network is employed to learn the mapping relationship between speckle image and curvature on the optimized dataset according to the optimized direction provided by principal component analysis. The testing results show that the prediction error of the trained model for the learned bending state is 5.910-4 m-1, and the prediction speed is 0.05 milliseconds per frame. The proposed scheme has a strong generalization ability and can be applied to predict bending states that have never been learned or seen with a prediction error of 3.810-2 m-1, which cannot be realized by the previously reported fiber specklegram sensor based on the classification neural network. The bending recognition scheme enhanced by deep learning proposed in this paper provides an enlightening reference for solving fiber sensing problems with deep learning methods, and it has the potential to be applied in more fields as a general scheme.

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“…P. Roldan-Varona et al designed a hybrid enveladding waveguide (CWG) Maheszende interferometric fiber sensor, which used femtosecond laser etching technology to write the fiber grating on the reference arm to realize [9] measurement of curvature and strain. Wei et al proposed a multi-parametric sensor, which first established the heterocore structure of multi-mode fiber-single-mode fiber-multi-mode fiber (MSM), and used laser CO2 to realize the sawtooth structure for sensing [10] of strain, temperature and torque. Using a few-mode fiber to sense [11] with temperature, strain and transverse force, Zhao et al According to the above research findings, the multi-parameter optical fiber sensor has had a relatively vigorous development.…”

Section: Introductionmentioning

confidence: 99%

FBG cascade EFPI multi-parameter optical fiber sensor based on GMPC coating

Liu,

Tao,

Feng

et al. 2023

AOPC 2023: Optic Fiber Gyro

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Magnetic sensitive material, giant magnetostrictive powder composite (GMPC), coated fiber bragg grating (FBG) cascaded extrinsic Fabry Pérot interferometer (EFPI) multi-parameter optical fiber sensor is proposed. Which enables the simultaneous detection of temperature, magnetic field and strain. The proposed sensor uses a double FBG cascade EFPI structure including GMPC coated FBG with a wavelength of 1550 nm to achieve magnetic field sensing. The results show that the sensor has a maximum temperature sensitivity of 175.4 pm/℃, a maximum magnetic field sensitivity of -1.54 pm/mT and a maximum stress sensitivity of -9.55 pm/με. The proposed multi-parameter optical fiber sensor solves the multi-parameter cross-sensitivity problem, while the sensor has the characteristics of low cost, interference resistance, compact structure, simple preparation process and long distance transmission. The sensor has promising applications in areas such as biomedicine, environmental detection and military applications, and oil well survey, industrial production, power systems and so on.

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Sawtooth Fiber MZ Vector Bending Sensor Available for Multi Parameter Measurement

Cited by 20 publications

References 24 publications

Multimode optical fiber sensors: from conventional to machine learning-assisted

Multimode optical fiber sensors: from conventional to machine learning-assisted

Bending recognition based on learning enhanced fiber specklegram sensor

FBG cascade EFPI multi-parameter optical fiber sensor based on GMPC coating

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