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Advanced Research of Optical Fiber Sensing Technology
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Advanced Research of Optical Fiber Sensing Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 14702

Special Issue Editors

Prof. Dr. Guolu Yin

E-Mail Website
Guest Editor
Professor, Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), Chongqing University, Chongqing 400044, China
Interests: optical sensors; distributed fiber sensing technology; optical frequency domain reflectometry; 3D shape sensing technology; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Biqiang Jiang

E-Mail Website
Guest Editor
Key Laboratory of Light Field Manipulation and Information Acquisition, Ministry of Industry and Information Technology, and Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710129, China
Interests: fiber optics; micro-nano fiber device; fiber sensing technology; biophotonics
Dr. Guo Nan

E-Mail Website
Guest Editor
Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), Chongqing University, Chongqing 400044, China
Interests: optical sensors; distributed fiber sensing technology; Brillouin optical time domain analysis/reflectometry

Special Issue Information

Dear Colleagues,

The capability of optical fiber sensors (OFS) has been amply revealed in the last few decades. This technology is currently in a rapid development stage and offers a high applied potential in the fields of perimeter security, structural health monitoring, nondestructive evaluation, and so on because of its advantages of high precision, electrical insulation, corrosion resistance, small size, and light weight.

This Special Issue will explore new designs, modem technology, and algorithm to improve the performance of all kinds of optical fiber sensors so as to meet increasingly high-performance demands in the applications. Potential topics of interest include, but are not limited to, the following:

  • Research frontier of distributed optical fiber sensors (DOFS) based on Raman, Rayleigh, or Brillouin scattering;
  • Fiber grating sensors;
  • Fiber interferometer sensor;
  • OFS based on specialty optical fiber ;
  • Novel signal processing methods;
  • Intelligent optical fiber sensing networks;
  • Advanced application of OFS.

Dr. Guolu Yin
Prof. Dr. Biqiang Jiang
Dr. Guo Nan
Guest Editors

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Published Papers (5 papers)

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Research

Jump to: Review

10 pages, 2498 KiB  
Article
Optical Frequency-Domain Reflectometry Based Distributed Temperature Sensing Using Rayleigh Backscattering Enhanced Fiber
by Ziyi Lu, Ting Feng, Fang Li and Xiaotian Steve Yao
Sensors 2023, 23(12), 5748; https://doi.org/10.3390/s23125748 - 20 Jun 2023
Cited by 6 | Viewed by 2393
Abstract
An innovative optical frequency-domain reflectometry (OFDR)-based distributed temperature sensing method is proposed that utilizes a Rayleigh backscattering enhanced fiber (RBEF) as the sensing medium. The RBEF features randomly high backscattering points; the analysis of the fiber position shift of these points before and [...] Read more.
An innovative optical frequency-domain reflectometry (OFDR)-based distributed temperature sensing method is proposed that utilizes a Rayleigh backscattering enhanced fiber (RBEF) as the sensing medium. The RBEF features randomly high backscattering points; the analysis of the fiber position shift of these points before and after the temperature change along the fiber is achieved using the sliding cross-correlation method. The fiber position and temperature variation can be accurately demodulated by calibrating the mathematical relationship between the high backscattering point position along the RBEF and the temperature variation. Experimental results reveal a linear relationship between temperature variation and the total position displacement of high backscattering points. The temperature sensing sensitivity coefficient is 7.814 μm/(m·°C), with an average relative error temperature measurement of −1.12% and positioning error as low as 0.02 m for the temperature-influenced fiber segment. In the proposed demodulation method, the spatial resolution of temperature sensing is determined by the distribution of high backscattering points. The temperature sensing resolution depends on the spatial resolution of the OFDR system and the length of the temperature-influenced fiber. With an OFDR system spatial resolution of 12.5 μm, the temperature sensing resolution reaches 0.418 °C per meter of RBEF under test. Full article
(This article belongs to the Special Issue Advanced Research of Optical Fiber Sensing Technology)
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Figure 1

Figure 1
<p>(<b>a</b>) OFDR system; (<b>b</b>) Distribution of scattering intensity in RBEF measured by OFDR.</p> Full article ">Figure 2
<p>Distributed temperature sensing demodulation principle and procedures using RBEF.</p> Full article ">Figure 3
<p>Desk temperature chamber and temperature analyzer for sensing and calibration experiments.</p> Full article ">Figure 4
<p>Results of temperature calibration experiments. (<b>a</b>) Relationship between displacement of high RBS peaks and corresponding positions along the RBEF under different temperatures; (<b>b</b>) Relationship between total displacement of temperature-influenced RBS peaks and temperature variations.</p> Full article ">Figure 5
<p>Accurately locating the temperature-influenced fiber segment using the high RBS peak displacement data measured at 40.19 °C in <a href="#sensors-23-05748-f004" class="html-fig">Figure 4</a>a.</p> Full article ">Figure 6
<p>Relationship between displacements and positions of high RBS peaks from the temperature sensing validation experiment.</p> Full article ">Figure 7
<p>Relationship between displacement of RBS peaks in RBEF and fiber position under constant temperature conditions.</p> Full article ">
13 pages, 8679 KiB  
Article
Optical Frequency Domain Reflectometry Based on Multilayer Perceptron
by Guolu Yin, Zhaohao Zhu, Min Liu, Yu Wang, Kaijun Liu, Kuanglu Yu and Tao Zhu
Sensors 2023, 23(6), 3165; https://doi.org/10.3390/s23063165 - 16 Mar 2023
Cited by 6 | Viewed by 2429
Abstract
We proposed an optical frequency domain reflectometry based on a multilayer perceptron. A classification multilayer perceptron was applied to train and grasp the fingerprint features of Rayleigh scattering spectrum in the optical fiber. The training set was constructed by moving the reference spectrum [...] Read more.
We proposed an optical frequency domain reflectometry based on a multilayer perceptron. A classification multilayer perceptron was applied to train and grasp the fingerprint features of Rayleigh scattering spectrum in the optical fiber. The training set was constructed by moving the reference spectrum and adding the supplementary spectrum. Strain measurement was employed to verify the feasibility of the method. Compared with the traditional cross-correlation algorithm, the multilayer perceptron achieves a larger measurement range, better measurement accuracy, and is less time-consuming. To our knowledge, this is the first time that machine learning has been introduced into an optical frequency domain reflectometry system. Such thoughts and results would bring new knowledge and optimization to the optical frequency domain reflectometer system. Full article
(This article belongs to the Special Issue Advanced Research of Optical Fiber Sensing Technology)
►▼ Show Figures

Figure 1

Figure 1
<p>Wavelength shift calculated by cross−correlation algorithm at strains of 1500, 2000, and 2250 με.</p> Full article ">Figure 2
<p>Schematic diagram of training phase and testing phase of multilayer perceptron.</p> Full article ">Figure 3
<p>Generation of training set samples in the multilayer perceptron.</p> Full article ">Figure 4
<p>(<b>a</b>)Experimental spectrum and (<b>b</b>) its Rayleigh scattering distribution; (<b>c</b>) training Rayleigh scattering spectrum and (<b>d</b>) its Rayleigh scattering distribution.</p> Full article ">Figure 5
<p>Comparison of free spectra range between experimental and training spectra at strain of (<b>a</b>) 100, (<b>b</b>) 1000, (<b>c</b>) 2000, and (<b>d</b>) 2900 με: L, spatial resolution.</p> Full article ">Figure 6
<p>Schematic of the optical frequency domain reflectometry system.</p> Full article ">Figure 7
<p>Comparison of the accuracy with different (<b>a</b>,<b>b</b>) training functions and (<b>c</b>,<b>d</b>) activation functions.</p> Full article ">Figure 8
<p>(<b>a</b>) Accuracy of multilayer perceptron with different numbers of layers; (<b>b</b>) ratio of training time of 7-hidden-layer and 6-hidden-layer to 5-hidden-layer network, respectively.</p> Full article ">Figure 9
<p>The structure of the proposed multilayer perceptron.</p> Full article ">Figure 10
<p>Confusion matrix of multilayer perceptron at strain from (<b>a</b>) 60 to 100 με and (<b>b</b>) 100 to 500 με.</p> Full article ">Figure 11
<p>(<b>a</b>) Mean absolute error of multilayer perceptron and cross-correlation algorithm at strain from 2500 to 2900 με; (<b>b</b>) confusion matrix of multilayer perceptron.</p> Full article ">
12 pages, 3123 KiB  
Article
Digital Control and Demodulation Algorithm for Compact Open-Loop Fiber-Optic Gyroscope
by Lin Chen, Zhao Huang, Yuzheng Mao, Biqiang Jiang and Jianlin Zhao
Sensors 2023, 23(3), 1473; https://doi.org/10.3390/s23031473 - 28 Jan 2023
Cited by 1 | Viewed by 2919
Abstract
With the advantages of small size, low cost, and moderate accuracy, an open-loop fiber-optic gyroscope (FOG) has a wide range of applications around control and automation. For the most cost-sensitive applications, a simple and stable digital algorithm with a reduced control-circuit volume and [...] Read more.
With the advantages of small size, low cost, and moderate accuracy, an open-loop fiber-optic gyroscope (FOG) has a wide range of applications around control and automation. For the most cost-sensitive applications, a simple and stable digital algorithm with a reduced control-circuit volume and cost is highly desirable to realize high-precision control of a FOG. In this work, a new algorithm for an open-loop FOG is proposed based on the discrete multi-point demodulation in the sinusoidal modulation period. Utilizing this algorithm, stable control and angular velocity calculation of a gyro are realized with effectively suppressed gyro error. The use of this algorithm greatly reduces the requirements for processing power and simplifies the gyro circuit. Based on this algorithm, a digital FOG with a volume of only 25 × 20 × 40 mm3 achieves a bias instability of less than 0.15°/h, an angle random walk (ARW) of less than 0.015°/√h, a start-up time of less than 1 s, and a 3 dB bandwidth beyond 160 Hz. This low-cost, compact, and high-performance gyro is sufficient to satisfy the requirements of applications in the navigation and control fields such as unmanned driving. Full article
(This article belongs to the Special Issue Advanced Research of Optical Fiber Sensing Technology)
►▼ Show Figures

Figure 1

Figure 1
<p>Scheme layout of an open-loop FOG.</p> Full article ">Figure 2
<p>Modulation and discrete sampling of the normalized interference signal.</p> Full article ">Figure 3
<p>Optimized picking interval for the elimination of the synchronous sampling delay, <span class="html-italic">τ</span>, at different modulation amplitudes.</p> Full article ">Figure 4
<p>(<b>a</b>) Diagram of PZT control; (<b>b</b>) periodically flipped driving pattern, the modulation generated by the driving, and the corresponding interference signal of the gyro (assuming that the gyro has a Sagnac phase shift of 0.05 rad for clarity).</p> Full article ">Figure 5
<p>(<b>a</b>) Circuit hardware diagram, (<b>b</b>) picture of the circuit hardware, (<b>c</b>) software process of FOG, and (<b>d</b>) picture of the FOG.</p> Full article ">Figure 6
<p>Dynamic control results of (<b>a</b>) SLED, (<b>b</b>) PZT, and (<b>c</b>) sampling delay during the startup process.</p> Full article ">Figure 7
<p>The measured results for our digital open-loop fiber-optic gyroscope. (<b>a</b>) Gyro output at different angular rates, (<b>b</b>) results for Allan variance testing, (<b>c</b>) gyro amplitude-frequency characteristics, and (<b>d</b>) bias comparison with the different algorithms.</p> Full article ">
11 pages, 634 KiB  
Article
Submetric Spatial Resolution ROTDR Temperature Sensor Assisted by Wiener Deconvolution
by Wenhao Zhu, Haoting Wu, Weixuan Chen, Meiting Zhou, Guolu Yin, Nan Guo and Tao Zhu
Sensors 2022, 22(24), 9942; https://doi.org/10.3390/s22249942 - 16 Dec 2022
Cited by 11 | Viewed by 2299
Abstract
A submetric spatial resolution Raman optical time-domain reflectometry (ROTDR) temperature sensor assisted by the Wiener deconvolution postprocessing algorithm has been proposed and experimentally demonstrated. Without modifying the typical configuration of the ROTDR sensor and the adopted pump pulse width, the Wiener demodulation algorithm [...] Read more.
A submetric spatial resolution Raman optical time-domain reflectometry (ROTDR) temperature sensor assisted by the Wiener deconvolution postprocessing algorithm has been proposed and experimentally demonstrated. Without modifying the typical configuration of the ROTDR sensor and the adopted pump pulse width, the Wiener demodulation algorithm is able to recover temperature perturbations of a smaller spatial scale by deconvoluting the acquired Stokes and anti-Stokes signals. Numerical simulations have been conducted to analyze the spatial resolution achieved by the algorithm. Assisted by the algorithm, a typical ROTDR sensor adopting pump pulses of 20 ns width can realize the distributed temperature sensing with a spatial resolution of 0.5 m and temperature accuracy of 1.99 °C over a 2.1-km sensing fiber. Full article
(This article belongs to the Special Issue Advanced Research of Optical Fiber Sensing Technology)
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Figure 1

Figure 1
<p>Schematic configuration of a generic ROTDR temperature sensor system. WDM, wavelength division multiplexer; DAQ, data acquisition.</p> Full article ">Figure 2
<p>(<b>a</b>) Numerical generated degradation functions with the same FWHM of 20 ns but different rising/falling edges of 7.6, 4.8, and 2.4 ns, (<b>b</b>) Rising edges of the corresponding simulated Raman signals.</p> Full article ">Figure 3
<p>Evolution of the 10/90% rising edge time of simulated deconvoluted Raman signals over parameter <span class="html-italic">K</span> for <math display="inline"><semantics> <mrow> <msub> <mo>ψ</mo> <mo>†</mo> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with FWHM of 20 ns and rising/falling edges of 7.6, 4.8, and 2.4 ns. Inset: (<b>a</b>) Rising edges of the simulated Raman signals for different <span class="html-italic">K</span> values and <math display="inline"><semantics> <mrow> <msub> <mo>ψ</mo> <mo>†</mo> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with FWHM of 20 ns and rising/falling edge of 7.6 ns. (<b>b</b>) Rising edges of the simulated Raman signals for the same hl<math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mo>ψ</mo> <mo>†</mo> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with FWHM of 20 ns and rising/falling edges of 7.6, 4.8, and 2.4 ns.</p> Full article ">Figure 4
<p>Experimental setup of ROTDR system. AOM, acoustic-optic modulator; EDFA, erbium-doped fiber amplifier; WDM, wavelength division multiplexer; MMF, multimode optical fiber; APD, avalanche photodiode; DAQ, data acquisition card; PC, personal computer.</p> Full article ">Figure 5
<p>The measured degradation function of a 20-ns pulse for our experimental ROTDR setup (blue curve) and the numerically generated degradation function of 18 ns for simulations (red curve).</p> Full article ">Figure 6
<p>Rising edge of (<b>a</b>) the original Stokes Raman signal without using the Wiener deconvolution and the estimated Stokes Raman signals using the Wiener deconvolution with (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>8</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>. The blue curves are results based on experimental data, and the red curves are simulated results.</p> Full article ">Figure 7
<p>Evolution of the rising edges of the estimated Raman signals based on both experimental and simulated data over parameter <span class="html-italic">K</span>.</p> Full article ">Figure 8
<p>The resolved temperature distributions around the heated 0.5 m fiber coil under different values of parameter <span class="html-italic">K</span>.</p> Full article ">Figure 9
<p>The resolved temperature distributions around the heated 9-m fiber coil (<b>a</b>) without deconvolution, (<b>b</b>) deconvoluted with parameter <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>8</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, (<b>c</b>) deconvoluted with parameter <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>3</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, (<b>d</b>) deconvoluted with parameter <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>Evolution of the standard deviation and absolute error of the resolved temperature distribution around the heated 9-m fiber coil over parameter <span class="html-italic">K</span>.</p> Full article ">

Review

Jump to: Research

40 pages, 30594 KiB  
Review
Hybrid Distributed Optical Fiber Sensor for the Multi-Parameter Measurements
by Xiao Zhou, Feng Wang, Chengyu Yang, Zijing Zhang, Yixin Zhang and Xuping Zhang
Sensors 2023, 23(16), 7116; https://doi.org/10.3390/s23167116 - 11 Aug 2023
Cited by 7 | Viewed by 3526
Abstract
Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines [...] Read more.
Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications. Full article
(This article belongs to the Special Issue Advanced Research of Optical Fiber Sensing Technology)
►▼ Show Figures

Figure 1

Figure 1
<p>The basic configuration of OTDR. DAQ: data-acquisition card.</p> Full article ">Figure 2
<p>The basic configuration of COTDR.</p> Full article ">Figure 3
<p>The typical configuration of BOTDR with coherent detection. EOM: electro-optic modulator; EDFA: erbium-doped optical fiber amplifier.</p> Full article ">Figure 4
<p>The typical configuration of BOTDA. ISO: isolator.</p> Full article ">Figure 5
<p>The typical configuration of ROTDR. WDM: wavelength-division multiplexer; APD: avalanche photodiode.</p> Full article ">Figure 6
<p>(<b>a</b>) Diagram of the frequency-scanning pump pulses and the probe wave. (<b>b</b>) Frequency relationship between the Rayleigh and the Brillouin scattering signals (reprinted from [<a href="#B199-sensors-23-07116" class="html-bibr">199</a>], under the terms and conditions of the Creative Commons Attribution License).</p> Full article ">Figure 7
<p>Experimental setup of the hybrid Φ-OTDR/BOTDA system (reprinted from [<a href="#B199-sensors-23-07116" class="html-bibr">199</a>], under the terms and conditions of the Creative Commons Attribution License).</p> Full article ">Figure 8
<p>Experimental setup of the hybrid Φ-OTDR/BOTDA system (reprinted from [<a href="#B192-sensors-23-07116" class="html-bibr">192</a>], under the Open Access Publishing Agreement from © 2021 Optical Society of America).</p> Full article ">Figure 9
<p>(<b>a</b>) Experimental setup of the hybrid Φ-OTDR/BOTDR system. (<b>b</b>) Modulated pulse sequences and the corresponding acquired signals (reprinted from [<a href="#B193-sensors-23-07116" class="html-bibr">193</a>], under the Open Access Publishing Agreement from © 2016 Optical Society of America).</p> Full article ">Figure 10
<p>Experimental setup of the hybrid Φ-OTDR/BOTDR system (reprinted from [<a href="#B145-sensors-23-07116" class="html-bibr">145</a>], under the Open Access Publishing Agreement from © 2022 Optica Publishing Group).</p> Full article ">Figure 11
<p>Experimental setup of the hybrid Φ-OTDR/BOTDA system through space-division multiplexing (SDM) based on the multi-core fiber (MCF) (reprinted from [<a href="#B200-sensors-23-07116" class="html-bibr">200</a>], under the Open Access Publishing Agreement from © 2017 Optical Society of America).</p> Full article ">Figure 12
<p>Experimental setup of the hybrid Φ-OTDR/BOTDA system combining multiplexing and distributed amplification techniques (reprinted from [<a href="#B194-sensors-23-07116" class="html-bibr">194</a>], under the terms and conditions of the Creative Commons Attribution License).</p> Full article ">Figure 13
<p>Experimental setup of the hybrid single-end-access BOTDA and COTDR system (reprinted from [<a href="#B195-sensors-23-07116" class="html-bibr">195</a>] with permission, © 2013 IEEE).</p> Full article ">Figure 14
<p>Experimental setup of the hybrid BOTDA/COTDR system (adapted from [<a href="#B197-sensors-23-07116" class="html-bibr">197</a>]).</p> Full article ">Figure 15
<p>Experimental setup of the hybrid Rayleigh and Brillouin system (reprinted from [<a href="#B172-sensors-23-07116" class="html-bibr">172</a>], under the Open Access Publishing Agreement from © 2023 Optica Publishing Group).</p> Full article ">Figure 16
<p>(<b>a</b>) Experimental setup of the hybrid BOTDR/COTDR system; (<b>b</b>) schematic representation of the detecting signal spectrum according to the temperature or strain change (adapted from [<a href="#B198-sensors-23-07116" class="html-bibr">198</a>]).</p> Full article ">Figure 17
<p>Experimental setup of the hybrid Φ-OTDR/ROTDR system using a commercial off-the-shelf DFB laser and direct detection (reprinted from [<a href="#B205-sensors-23-07116" class="html-bibr">205</a>] with permission, © 2016 Optical Society of America).</p> Full article ">Figure 18
<p>Experimental setup of the hybrid Φ-OTDR/ROTDR system (reprinted from [<a href="#B206-sensors-23-07116" class="html-bibr">206</a>] with permission, © 2018 Elsevier).</p> Full article ">Figure 19
<p>Experimental setup of the hybrid Φ-OTDR/ROTDR system based on multi-core fiber (adapted from [<a href="#B208-sensors-23-07116" class="html-bibr">208</a>]).</p> Full article ">Figure 20
<p>Experimental setup of the hybrid BOTDA/ROTDR system using cyclic pulse coding (reprinted from [<a href="#B210-sensors-23-07116" class="html-bibr">210</a>] with permission, © 2013 Optical Society of America).</p> Full article ">Figure 21
<p>Experimental setup of the hybrid BOTDR/ROTDR system based on multi-core fiber (reprinted from [<a href="#B212-sensors-23-07116" class="html-bibr">212</a>], under the Open Access Publishing Agreement from © 2016 Optical Society of America).</p> Full article ">Figure 22
<p>Experimental setup of the hybrid BOTDR/POTDR system (adapted from [<a href="#B213-sensors-23-07116" class="html-bibr">213</a>]).</p> Full article ">Figure 23
<p>Experimental setup of the frequency-scanning Φ-OTDR (reprinted from [<a href="#B214-sensors-23-07116" class="html-bibr">214</a>] with permission, © 2015 IEEE).</p> Full article ">Figure 24
<p>Experimental setup of the single-end hybrid Φ-OTDR/BOTDA system (adapted from [<a href="#B215-sensors-23-07116" class="html-bibr">215</a>]).</p> Full article ">Figure 25
<p>(<b>a</b>) BFS profiles when the fiber end is heated; (<b>b</b>) temperature evolution over the heated section; (<b>c</b>) vibration measured by BOTDA; (<b>d</b>) vibration measured by Φ-OTDR (reprinted from [<a href="#B192-sensors-23-07116" class="html-bibr">192</a>], under the Open Access Publishing Agreement from © 2021 Optical Society of America).</p> Full article ">Figure 26
<p>(<b>a</b>) Fiber arrangement in the test; (<b>b</b>) demodulated phase signal corresponding to an 800 Hz triangular vibration; (<b>c</b>) enlarged view of the hotspot at different heated temperatures (reprinted from [<a href="#B145-sensors-23-07116" class="html-bibr">145</a>], under the Open Access Publishing Agreement from © 2022 Optical Society of America).</p> Full article ">Figure 27
<p>(<b>a</b>) BFS profiles along the whole fiber; (<b>b</b>) BFS profiles around the heated section; (<b>c</b>) demodulated Rayleigh signal along the whole fiber; (<b>d</b>) demodulated Rayleigh signal at the location of perturbation (reprinted from [<a href="#B194-sensors-23-07116" class="html-bibr">194</a>], under the terms and conditions of the Creative Commons Attribution License).</p> Full article ">Figure 28
<p>(<b>a</b>) Total of 885 superposed consecutive differential Φ-OTDR traces with intrusion applied to two fiber segments; (<b>b</b>) resolved temperature distribution with denoising method (adapted from [<a href="#B208-sensors-23-07116" class="html-bibr">208</a>], detailed curves are available in the reference paper).</p> Full article ">Figure 29
<p>(<b>a</b>) Measured Raman Stokes and anti-Stokes traces along the whole fiber under various temperatures; (<b>b</b>) temperature distribution near the heated fiber section (reprinted from [<a href="#B206-sensors-23-07116" class="html-bibr">206</a>] with permission, © 2018 Elsevier).</p> Full article ">Figure 30
<p>(<b>a</b>) Detected vibration waterfall regarding the knock event; (<b>b</b>) demodulated dynamic strain corresponding to the vibration exerting on the PZT; (<b>c</b>) frequency responses of the demodulated dynamic strain with different frequencies (reprinted from [<a href="#B206-sensors-23-07116" class="html-bibr">206</a>] with permission, © 2018 Elsevier).</p> Full article ">Figure 31
<p>(<b>a</b>) Diagram of the test bench for independent measurement of temperature and strain; (<b>b</b>) independent measurement of distributed temperature and strain (adapted from [<a href="#B198-sensors-23-07116" class="html-bibr">198</a>], detailed curves are available in the reference paper).</p> Full article ">Figure 32
<p>(<b>a</b>) Diagram of the good instrumentation for a distributed measurement of temperature and strain; (<b>b</b>) comparative curves of a distributed measurement among BOTDR DSTS (blue curve for temperature and purple curve for strain), standard ROTDR (red curve) and standalone BOTDR (yellow curve) (adapted from [<a href="#B198-sensors-23-07116" class="html-bibr">198</a>], detailed curves are available in the reference paper).</p> Full article ">Figure 33
<p>(<b>a</b>) Recovered temperature variation over time; (<b>b</b>) recovered strain variation over time; (<b>c</b>) amplitude spectral density curve of the recovered temperature; (<b>d</b>) amplitude spectral density curve of the recovered strain (reprinted from [<a href="#B172-sensors-23-07116" class="html-bibr">172</a>], under the Open Access Publishing Agreement from © 2023 Optica Publishing Group).</p> Full article ">Figure 34
<p>(<b>a</b>) Temperature distribution measured by Raman scattering; (<b>b</b>) strain resolution along the sensing fiber (reprinted from [<a href="#B210-sensors-23-07116" class="html-bibr">210</a>] with permission, © 2013 Optical Society of America).</p> Full article ">Figure 35
<p>(<b>a</b>) Absolute temperature distribution measured by BOTDA; (<b>b</b>) relative temperature distribution measured by Φ-OTDR; (<b>c</b>) estimated temperature uncertainty distribution of BOTDA; (<b>d</b>) estimated temperature uncertainty distribution of Φ-OTDR (reprinted from [<a href="#B200-sensors-23-07116" class="html-bibr">200</a>], under the Open Access Publishing Agreement from © 2017 Optical Society of America).</p> Full article ">Figure 36
<p>(<b>a</b>) Absolute strain change measured by BOTDA; (<b>b</b>) relative strain change of group 1 measured by Φ-OTDR; (<b>c</b>) relative strain change of group 2 measured by Φ-OTDR (reprinted from [<a href="#B199-sensors-23-07116" class="html-bibr">199</a>], under the terms and conditions of the Creative Commons Attribution License).</p> Full article ">
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