Abstract

We proposed and experimentally demonstrated a few mode fiber (FMF) based Raman distributed temperature sensor (RDTS) to extend the sensing distance with enhanced signal-to-noise ratio (SNR) of backscattered anti-Stokes spontaneous Raman scattering. Operating in the quasi-single mode (QSM) with efficient fundamental mode excitement, the FMF allows much larger input pump power before the onset of stimulated Raman scattering compared with the standard single mode fiber (SSMF) and mitigates the detrimental differential mode group delay (DMGD) existing in the conventional multimode fiber (MMF) based RDTS system. Comprehensive theoretical analysis has been conducted to reveal the benefits of RDTS brought by QSM operated FMFs with the consideration of geometric/optical parameters of different FMFs. The measurement uncertainty of FMF based scheme has also been evaluated. Among fibers being investigated and compared (SSMF, 2-mode and 4-mode FMFs, respectively), although an ideal 4-mode FMF based RDTS has the largest SNR enhancement in principle, real fabrication imperfections and larger splicing loss degrade its performance. While the 2-mode FMF based system outperforms in longer distance measurement, which agrees well with the theoretical calculations considering real experimental parameters. Using the conventional RDTS hardware, a 30-ns single pulse at 1550nm has been injected as the pump; the obtained temperature resolutions at 20km distance are estimated to be about 10°C, 7°C and 6°C for the SSMF, 4-mode and 2-mode FMFs, respectively. About 4°C improvement over SSMF on temperature resolution at the fiber end with 3m spatial resolution within 80s measuring time over 20km 2-mode FMFs have been achieved.

© 2017 Optical Society of America

1. Introduction

Raman distributed temperature sensor (RDTS) has been intensively investigated and widely used in industry as a commercial alternative solution for long distance temperature monitoring. The applications include fire alarm of large buildings (e.g. buildings containing air-conditioning systems), monitoring of pipelines, real time monitoring of high-power transmission cables and process control in industry [1]. In RDTS, the ratio of Raman anti-Stokes (AS) to Stokes (S) intensities is usually used for temperature estimation [2]. Temperature sensing along the fiber is generally achieved through optical time domain reflectometry (OTDR) technique, in which light pulses are coupled into the fiber, and spontaneous Raman scattering (SpRS) lights are detected [3].

However, the intensity of the SpRS is about 60-70 dB weaker than the input peak power of pump pulse [4]. Moreover, the maximum usable pump power is limited by optical nonlinearity which is dominated by stimulated Raman scattering (SRS) in meter-scale spatial resolution system. As a result, the obtained ultra-low signal-to-noise ratio (SNR) becomes the major limiting factor of the sensing performance of RDTS. Therefore, the standard single mode fiber (SSMF) based RDTS is a threshold limited system, consequently it features of short sensing range. To further enhance the measurement range, advanced techniques such as pulse coding and optical amplification have been proposed for SSMF based RDTS, yet make the system complicated and high cost [4-5].

As a solution, the multi-mode fiber (MMF) characterized by high SRS threshold power and larger backscattering coefficients is commonly employed in RDTS as the sensing fiber instead of SMF. Unfortunately, the sensing range of the system is normally limited to 10km due to the deterioration of spatial resolution induced by intermodal dispersion [6]. Although the intermodal dispersion can be partially overcome by using graded-index MMF, the best achievable spatial resolution is still limited [7].

In this paper, we present a performance enhanced RDTS scheme by using the few mode fiber (FMF). As a compromise of SSMF and MMF, the quasi-single mode (QSM) operation in FMF allows larger input pump power before the establishment of detrimental effects induced by fiber nonlinearities due to the well-controlled effective fundamental mode area. Moreover, FMF supports only a few spatial modes and the coupling between the fundamental mode and higher order modes can be largely suppressed with careful design [8]. By only exciting the fundamental mode in FMF, the spatial resolution deterioration caused by modal dispersion (the main limit of MMF based system performance) can be alleviated in the QSM operated FMF. The combination of improved system SNR and uniform spatial resolution along the FMF link is attractive to develop a long distance RDTS system without sacrificing sensing performance. Detailed theoretical analysis and experimental verifications have been conducted to validate the feasibility and advantages of FMF based RDTS. With the conventional RDTS hardware implementation, the performance of FMF system is mainly determined by the optical parameters of the used FMF. 2-mode and 4-mode FMFs are designed and fabricated for RDTS systems. Their sensing performances have been investigated theoretically and experimentally. In addition to the signal processing improvements, it has been demonstrated that the FMF based system can achieve much higher SNR of SpRS signals along the sensing links than SMF and can be further improved by optimization of few-mode fiber parameters. Compared with the conventional SMF based system, a 4°C temperature resolution improvement over 20km is achieved without degradation of spatial resolution.

2. Theory

RDTS is essentially a variant of an OTDR technique. A short laser pulse coupled in one end of the sensing fiber, propagates along the fiber with its intensity partially backscattered and guided back to the launching end. Due to SpRS resulting from interactions of the laser pulse with lattice vibration modes, a small fraction is backscattered to downshifted S light (with a frequency of vs) and upshifted AS light (with a frequency of vas) [9]. The AS component depends strongly on the fiber temperature at the scattering point, while the S component is practically insensitive to temperature variation. Typically, in RDTS system the temperature information is extracted from the ratio of these two backscattered Raman components to eliminate the errors caused by factors including intensity fluctuations of the pump light, fiber attenuation, variation of fiber core composition, and changes of the Raman capture coefficient along the fiber [10]:

RTPASPS(vasvs)4exp(hΔvkBT)
where PAS (PS) and vas (vs) are the power and frequency of AS (S) light, respectively, and Δv is the Raman frequency shift, h is the Planck constant, kB is the Boltzmann constant, and T is the absolute temperature.

Therefore, the demodulated temperature resolution of a RDTS is subject to the SNR of the SpRS light. The received power of AS light generated at the fiber length z can be expressed as [11]

PAS(z)=DFAS(z)gRP0×G(z)×exp{0z[αR(ς)+αAS(ς)]dς}
where αR and αAS are fiber attenuation coefficients at pump and AS wavelengths, respectively, gR is the Raman gain coefficient, D is a constant factor and FAS(z) is the temperature-dependent factor, P0 is the input pump light power, G(z) is Raman gain of probe light within pulse length. Considering a short pump light pulse launched into the fiber at z = 0, G(z) is given by [12]
G(z)=exp(gRP0(z)cΔt/2nAeff)
where c and Δt are the velocity of light in vacuum and the pump’s pulse width, respectively. Aeff is the mode effective area, P0(z) is the pump power at position z, and n is the refractive index of the optical fiber core. As can be seen from Eq. (2), PAS(z) is proportional to D and FAS (z). Since these two factors show no relevance with fiber parameters, to quantitatively investigate the effect of certain fiber parameters on system performance, we define

H(P0,z)gRP0×G(z)×exp{0z[αR(ς)+αAS(ς)]dς}=gRP0×exp(gRP0(z)cΔt/2nAeff)×exp{0z[αR(ς)+αAS(ς)]dς}

The input pump power P0 is limited to a given peak threshold value to avoid signal distortions caused by nonlinearities. For meter-scale spatial resolution system, the maximum of P0 is mainly hindered by SRS threshold, which is approximately proportional to the mode effective area of fiber according to the relation [11]

Pth20AeffgRLeff
where Leff is the effective length of the fiber.

Table 1 presents a specific set of measured parameters of the fibers in our experiment. gR of each fiber is obtained through experimental measurement. Since the gR values of these three fibers are very close, the distinctions of the gR are ignored in the following analysis. The 2-mode FMF used for our proposed RDTS system has grade-index profile, and the 4-mode FMF is step-index as shown in Fig. 1.

Tables Icon

Table 1. Measured parameters of the fibers under test

 figure: Fig. 1

Fig. 1 (a) Refractive index profile of the 2-mode FMF. (b) Refractive index profile of the 4-mode FMF.

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Due to the differences of Aeff among three fibers, the SRS threshold Pth are different, as well as the maximum P0 (corresponding to Pth). With the parameters in Table 1, in the ideal simulation (splicing loss ignored), H(P0, z) of these three fibers are calculated and shown in Fig. 2. It can be seen that H(P0, z) exhibits strong dependence on fiber types (with different Aeff, αR and αAS), resulting in different achievable AS power.

 figure: Fig. 2

Fig. 2 Calculated H (P0, z) for different fibers as a function of P0 and z.

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To further investigate the effects of fiber parameters (including Aeff, αR and αAS), more details are displayed in Fig. 3. When z = 0, Fig. 3(a) depicts the H curves as a function of the fiber input power P0. Under the same P0 (< Pth1), the H of SSMF is larger than the two FMFs thanks to its larger G(z) which is inversely proportional to Aeff. However, due to that P0 is limited to Pth1, H of SSMF can only reach Hth1. While the H of the two FMFs can overcome this limitation with larger Pth because of larger Aeff. And the 4-mode FMF can provide the largest H. Considering the transmission attenuation, H - P0 curves at the fiber end after 20km transmission are shown in Fig. 3(b). It indicates that the FMFs based RDTS can always provide better performance than SSMF over the whole fiber link if the input power P0 is set near their respective threshold Pth. To verify this, in Fig. 3(c), the H - z curves are illustrated for different fibers with P0 equal to their respective threshold Pth. The H of 2-mode FMF is always larger than that of the SSMF over the whole fiber length thanks to its larger Pth, and 4-mode FMF has the largest H among the three fibers for its largest threshold. Since PAS is proportional to H, it can be concluded that the ideal RDTS system based on FMFs can always reach higher SNR than SSMF along the fiber when the input pump power is set close to the corresponding SRS threshold.

 figure: Fig. 3

Fig. 3 (a) H - P0 curves when z = 0; Pth1: SRS threshold of SSMF; Pth2: SRS threshold of 2-mode FMF; Pth3: SRS threshold of 4-mode FMF; Hmax1: maximum H value of SSMF; Hmax2: maximum H value of 2-mode FMF; Hmax3: maximum H value of 4-mode FMF. (b) H - P0 curves when z = 20km. (c) Ideal H - z curves when P0 = Pth. (d) H - z curves for three different fibers when P0 = Pth and splicing loss is considered.

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To investigate the performance of FMF, the FMF is used as the sensing fiber in a conventional SMF-components based RDTS system. By central-alignment splicing SMF and FMF with a fusion taper to excite only the fundamental mode in FMF, QSM operation in FMF has been achieved. Thus, the SMF components can compatibly work with FMF with low splicing loss. The splicing of SMF and FMF is controlled by optimized splicing program in the splicer, which ensures repeatable splicing. As a result, the splicing loss in repeated experiments has been verified to be stable around a specific value for a given FMF. The whole splicing loss induced by mode-field mismatch between SMF and FMF at the taper region is different between the 2-mode and 4-mode FMFs as shown in Table 1. The splicing loss of the 2-mode FMF can be reduced to less than 0.6dB, much lower than the 4-mode FMF owing to its smaller core diameter and graded index distribution that helps to gather the mode field to the center of core. Considering the real experimental splicing loss, the calculated H - z curves are plotted in Fig. 3(d). In this case, the H of the 4-mode FMF reduces a lot than Fig. 3(c) due to its larger splicing loss and turns out to be lower than the 2-mode FMF after 1km transmission owning to larger αR and αAS. As indicated by Figs. 3(c) and 3(d), compared with SSMF, the performance of FMFs based systems is mainly limited by larger transmission loss in long-range sensing, especially for 4-mode FMF. Fortunately, this problem can be well settled by optimizing the design and fabrication of the FMF.

3. Experiment

To evaluate the performances of the RDTS systems, the experimental setup shown in Fig. 4 has been implemented. The Raman-based DTS system consists of three sets of fiber under test (FUT), a high-power pulsed laser, a commercial Raman filter and avalanche photodiode (APD) receivers. The three FUTs are SSMF, 4-mode FMF and 2-mode FMF, respectively, each of which consists of seven sections with length of 5km, 150m, 5km, 150m, 5km, 150m and 4.55km. The high-power pulsed laser source operates at about 1550nm wavelength with a line-width of 0.5nm, a pulse width of 30ns (corresponding to a spatial resolution of 3m, in principle) and a repetition rate of 2.5 kHz. The pulses are launched into the sensing fiber through a circulator. And the backscattered lights from the circulator are separated into AS and S lights by a Raman filter, whose measured transmission spectra for the anti-Stokes and Stokes light are presented by inset.1 in Fig. 4. The isolation degree ranges from 35dB up to 40dB for anti-stokes port, which permits the effective separation of anti-Stokes and Rayleigh-scattering light. The S and AS lights are then detected simultaneously by two high-sensitivity low-noise avalanche photodiodes based 50MHz bandwidth photodetectors. Then the electrical signals are averaged 60000 times during 80 seconds by a high-speed data acquisition (DAQ) card with a 100 MS/s sampling rate, and finally transmitted to the PC for further data processing.

 figure: Fig. 4

Fig. 4 Experimental setup of the RDTS system; WDM: wavelength division multiplex; APD: avalanche photo diode; DAQ: data acquisition; Inset.1: Transmittance spectra at WDM output; Inset.2: microscope image of taper spliced area.

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To only excite the fundamental mode in FMF and avoid the spatial resolution deterioration caused by modal dispersion, the FMF and SMF are central-alignment spliced with a fusion taper, whose microscope image is shown by inset. 2 in Fig. 4. By using a mode-field measuring device based on variable aperture technique, the acquired Aeff and the far-field intensity distribution (about 5 m transmission after the splicing taper) of the 2-mode and 4-mode FMF are respectively shown in Figs. 5(a) and 5(b). Figures 5(c) and 5(d) present the electric field intensity profiles of the core modes of the two FMFs acquired by simulations using COMSOL Multiphysics. It indicates that the measured Aeff of the far-field intensity distribution basically agree with the simulated Aeff of the fundamental mode, which means the high order modes are successfully suppressed with this taper splicing process, and only the fundamental mode has been excited in the FMFs.

 figure: Fig. 5

Fig. 5 (a) The measured far-field intensity distribution of 2-mode FMF. (b) The measured far-field intensity distribution of 4-mode FMF. (c) Electric field intensity profile of LP01 and LP11 in 2-mode FMF from simulation. (d) Electric field intensity profile of LP01, LP11, LP02 and LP21 in 4-mode FMF from simulation.

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To verify the SRS threshold enhancement provided by FMF, we carry out a contrast experiment with different sensing fiber, using the same input pump power slightly lower than the SRS threshold of 4-mode FMF. Figures 6(a) and 6(b) present the obtained S traces and AS traces with all sensing fiber kept at room temperature. It’s obvious that the AS and S traces of SSMF and 2-mode FMF are seriously distorted by SRS, thus unable to perform a successful temperature estimation. In contrast, the traces of the 4-mode FMF show typical spontaneous Raman scattering feature.

 figure: Fig. 6

Fig. 6 (a) Acquired backscattered stokes signal of different sensing fibers. (b) Acquired backscattered anti-stokes signal of different sensing fibers.

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To make a sensible performance comparison between the conventional and the proposed scheme, another contrast experiment is conducted, in which the input pump powers of these different fibers are set just slightly below their corresponding SRS thresholds. The sensing fiber sections A, B and C with a length of 150m are placed in a temperature controlled chamber (TCC), while the other sections are maintained at room temperature (around 29°C). Figure 7(a) shows a comparison of the averaged AS signals of different sensing fibers at room temperature. The SNR traces of each averaged AS signals are presented in Fig. 7(b). To improve the system performance, the averaged AS signals are processed with wavelet transform (WT) denoising, as shown in Fig. 7(c), with the corresponding SNR traces of WT de-noised signals plotted in Fig. 7(d). The WT is a method that decomposes the signal into various frequency bands, in which the lower frequency range represents the most useful part of Raman signals and the high-frequency components above a certain frequency level represent noise [13]. The outcome of WT operation is a set of wavelet coefficients, among which the smaller coefficients towards zero representing the noise components are reduced by soft thresholding approach in the WT denoising process [14]. Comparing Figs. 7(b) and 7(d), it can be seen that by using the WT denoising method an enhancement in SNR of 10 dB can be achieved for the backscattered signals.

 figure: Fig. 7

Fig. 7 (a) Averaged AS traces without WT denoising. (b) SNR traces of the averaged AS traces without WT denoising. (c) WT de-noised AS traces. (d) SNR traces of the WT de-noised AS traces.

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As indicated in Fig. 7(a), the detected AS powers exhibit strong dependence on fiber type with different Pth, αR, αAS and splicing loss, and the results are well consistent with theoretical predictions. The AS traces of FMFs show a sharper downward character than SSMF due to their larger transmission loss. However, as can be seen in Fig. 7(b), despite of the defects of larger transmission loss, the received backscattered AS power of the FMFs is always higher than SSMF thanks to their larger Pth. In this case, it allows for significant increase in SNR of about 10 dB at the fiber end using the 2-mode FMF, and about 5 dB with 4-mode FMF in comparison with that of the SSMF. It can be seen that the acquired AS signal of the 2-mode FMF is higher than the 4-mode FMF due to its smaller transmission and splicing loss. That implies further enhancement in SNR can be obtained by reducing αR, αAS and splicing loss of FMFs.

Figures 8(a)–8(c) show the temperature profiles obtained with the three different sensing fibers based RDTS, respectively. The temperature of the TCC is set at 50°C, 70°C and 90°C. The temperature resolution distributions are obtained by calculating the root mean square (RMS) of the measured temperature along a window of 50m [15], as shown in Fig. 8(d). As expected, the SSMF-based RDTS has a poorer temperature resolution, of about 10°C at 20 km distance, while 4-mode and 2-mode FMF achieve better temperature resolution of about 7°C and 6°C at the same position. Besides, to achieve better than 2°C temperature resolution, the sensing range of SSMF-based system is limited to 6km. While another 6km sensing range can be extended with the 2-mode FMF.

 figure: Fig. 8

Fig. 8 (a) Temperature-distance trace of SSMF. (b)Temperature-distance trace of the 2-mode FMF. (c) Temperature-distance trace of the 4-mode FMF. (d) RMS temperature resolutions for different fiber types.

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The FMF-based RDTS systems have better temperature resolution than SSMF-based system mainly due to the larger SRS threshold, which allows for larger input pump power resulting in higher SNR. Moreover, the 2-mode FMF system shows distinct advantage over the 4-mode FMF system because of its smaller transmission and splicing loss. Therefore, system performance can be further improved by optimizing these factors. Fortunately, the fiber transmission loss could be overcome by optimizing αR and αAS of the FMF after finer control of fabrication, and the splicing loss can be reduced by optimizing the process of fusion taper splicing.

As aforementioned, mode dispersion can deteriorate the spatial resolution after long haul transmission. To verify the improvement of our QSM system’s performance in terms of spatial resolution, a step-like temperature profile at about 15km heated section is shown in Fig. 9. The measured spatial resolution (response distance corresponding to 10%–90% temperature step) of FMF based systems are the same as that of the SSMF system, as shown in Figs. 9(a)–9(c), respectively, which matches with the pulse width of 30ns. The mitigation of spatial resolution limitation in FMFs potentially allows for long-range sensing with sub-meter spatial resolution (ask for ≤ 10 ns pulse width), which is in practice impossible with conventional RDTS systems based on MMFs.

 figure: Fig. 9

Fig. 9 (a) Spatial resolutions near 15.5 km of SSMF. (b) Spatial resolutions near 15.5 km of the 2-mode FMF. (c) Spatial resolutions near 15.5 km of the 4-mode FMF.

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4. Conclusion

In conclusion, we demonstrate a FMF based RDTS system with enhanced SNR of SpRS to extend the sensing distance. The QSM operated FMF allows for much larger input pump power before the initiate of SRS compared with SSMF and mitigates the DMGD existing in the conventional MMF based RDTS system. Using the conventional RDTS hardware, temperature monitoring along three different sensing fibers with 3m spatial resolution within 80s measuring time over 20km fiber link have been achieved. The SSMF-based RDTS has the worst temperature resolution, of about 10°C at 20 km distance, while 4-mode and 2-mode FMF achieve better temperature resolution of about 7°C and 6°C at the same position. Our proposed FMF-based technique provides a promising solution for long distance RDTS using dedicated sensing and transmission fiber medium. The system performance can be further improved by optimizing the optical parameters of FMFs in addition with advanced coding and denoising technologies.

Funding

National Natural Science Foundation of China (NSFC) (61331010); 863 High Technology Plan of China (2013AA013402); Program for New Century Excellent Talents in University (NCET-13-0235).

References and links

1. “Technology Focus: Optical-fiber sensors,” Nat. Photonics2(3), 143–158 (2008).

2. B. Culshaw, “Optical fiber sensor technologies: opportunities and-perhaps-pitfalls,” J. Lightwave Technol. 22(1), 39–50 (2004). [CrossRef]  

3. A. Signorini, S. Faralli, M. A. Soto, G. Sacchi, F. Baronti, R. Barsacchi, A. Lazzeri, R. Roncella, G. Bolognini, and F. Di Pasquale, “40 km long-range Raman-based distributed temperature sensor with meter-scale spatial resolution,” in Optical Fiber Communication Conference, OSA Technical Digest (CD) (Optical Society of America, 2010), paper OWL2. [CrossRef]  

4. M. A. Soto, T. Nannipieri, A. Signorini, A. Lazzeri, F. Baronti, R. Roncella, G. Bolognini, and F. Di Pasquale, “Raman-based distributed temperature sensor with 1 m spatial resolution over 26 km SMF using low-repetition-rate cyclic pulse coding,” Opt. Lett. 36(13), 2557–2559 (2011). [CrossRef]   [PubMed]  

5. G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007). [CrossRef]  

6. L. Thévenaz, “Review and progress on distributed fiber sensing,” in Optical Fiber Sensors, OSA Technical Digest (CD) (Optical Society of America, 2006), paper ThC1.

7. X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012). [CrossRef]   [PubMed]  

8. A. Li, Y. Wang, Q. Hu, D. Che, X. Chen, and W. Shieh, “Measurement of distributed mode coupling in a few-mode fiber using a reconfigurable Brillouin OTDR,” Opt. Lett. 39(22), 6418–6421 (2014). [CrossRef]   [PubMed]  

9. D. A. Long, Raman Spectroscopy, (McGraw-Hill, 1977).

10. X. Sun, J. Li, and M. J. Hines, “Distributed temperature measurement using a dual-core fiber with an integrated miniature turn-around,” Proc. SPIE 9852, 98520R (2016). [CrossRef]  

11. G. P. Agrawal, Nonlinear Fiber Optics, 3rd ed. (Academic, 2001).

12. Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007). [CrossRef]  

13. M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015). [CrossRef]  

14. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41(3), 613–627 (1995). [CrossRef]  

15. Z. Zhao, Y. Dang, M. Tang, L. Duan, M. Wang, H. Wu, S. Fu, W. Tong, P. P. Shum, and D. Liu, “Spatial-division multiplexed hybrid Raman and Brillouin optical time-domain reflectometry based on multi-core fiber,” Opt. Express 24(22), 25111–25118 (2016). [CrossRef]   [PubMed]  

References

  • View by:

  1. “Technology Focus: Optical-fiber sensors,” Nat. Photonics2(3), 143–158 (2008).
  2. B. Culshaw, “Optical fiber sensor technologies: opportunities and-perhaps-pitfalls,” J. Lightwave Technol. 22(1), 39–50 (2004).
    [Crossref]
  3. A. Signorini, S. Faralli, M. A. Soto, G. Sacchi, F. Baronti, R. Barsacchi, A. Lazzeri, R. Roncella, G. Bolognini, and F. Di Pasquale, “40 km long-range Raman-based distributed temperature sensor with meter-scale spatial resolution,” in Optical Fiber Communication Conference, OSA Technical Digest (CD) (Optical Society of America, 2010), paper OWL2.
    [Crossref]
  4. M. A. Soto, T. Nannipieri, A. Signorini, A. Lazzeri, F. Baronti, R. Roncella, G. Bolognini, and F. Di Pasquale, “Raman-based distributed temperature sensor with 1 m spatial resolution over 26 km SMF using low-repetition-rate cyclic pulse coding,” Opt. Lett. 36(13), 2557–2559 (2011).
    [Crossref] [PubMed]
  5. G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
    [Crossref]
  6. L. Thévenaz, “Review and progress on distributed fiber sensing,” in Optical Fiber Sensors, OSA Technical Digest (CD) (Optical Society of America, 2006), paper ThC1.
  7. X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
    [Crossref] [PubMed]
  8. A. Li, Y. Wang, Q. Hu, D. Che, X. Chen, and W. Shieh, “Measurement of distributed mode coupling in a few-mode fiber using a reconfigurable Brillouin OTDR,” Opt. Lett. 39(22), 6418–6421 (2014).
    [Crossref] [PubMed]
  9. D. A. Long, Raman Spectroscopy, (McGraw-Hill, 1977).
  10. X. Sun, J. Li, and M. J. Hines, “Distributed temperature measurement using a dual-core fiber with an integrated miniature turn-around,” Proc. SPIE 9852, 98520R (2016).
    [Crossref]
  11. G. P. Agrawal, Nonlinear Fiber Optics, 3rd ed. (Academic, 2001).
  12. Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007).
    [Crossref]
  13. M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
    [Crossref]
  14. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41(3), 613–627 (1995).
    [Crossref]
  15. Z. Zhao, Y. Dang, M. Tang, L. Duan, M. Wang, H. Wu, S. Fu, W. Tong, P. P. Shum, and D. Liu, “Spatial-division multiplexed hybrid Raman and Brillouin optical time-domain reflectometry based on multi-core fiber,” Opt. Express 24(22), 25111–25118 (2016).
    [Crossref] [PubMed]

2016 (2)

2015 (1)

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

2014 (1)

2012 (1)

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

2011 (1)

2007 (2)

G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
[Crossref]

Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007).
[Crossref]

2004 (1)

1995 (1)

D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41(3), 613–627 (1995).
[Crossref]

Arya, R.

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Bao, X.

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

Baronti, F.

Bolognini, G.

M. A. Soto, T. Nannipieri, A. Signorini, A. Lazzeri, F. Baronti, R. Roncella, G. Bolognini, and F. Di Pasquale, “Raman-based distributed temperature sensor with 1 m spatial resolution over 26 km SMF using low-repetition-rate cyclic pulse coding,” Opt. Lett. 36(13), 2557–2559 (2011).
[Crossref] [PubMed]

G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
[Crossref]

Che, D.

Chen, L.

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

Chen, X.

Culshaw, B.

Dang, Y.

Di Pasquale, F.

M. A. Soto, T. Nannipieri, A. Signorini, A. Lazzeri, F. Baronti, R. Roncella, G. Bolognini, and F. Di Pasquale, “Raman-based distributed temperature sensor with 1 m spatial resolution over 26 km SMF using low-repetition-rate cyclic pulse coding,” Opt. Lett. 36(13), 2557–2559 (2011).
[Crossref] [PubMed]

G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
[Crossref]

Donoho, D. L.

D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41(3), 613–627 (1995).
[Crossref]

Duan, L.

Fu, S.

Gong, Y.

Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007).
[Crossref]

Hao, J.

Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007).
[Crossref]

Hines, M. J.

X. Sun, J. Li, and M. J. Hines, “Distributed temperature measurement using a dual-core fiber with an integrated miniature turn-around,” Proc. SPIE 9852, 98520R (2016).
[Crossref]

Hu, Q.

Kher, S.

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Lazzeri, A.

Li, A.

Li, J.

X. Sun, J. Li, and M. J. Hines, “Distributed temperature measurement using a dual-core fiber with an integrated miniature turn-around,” Proc. SPIE 9852, 98520R (2016).
[Crossref]

Liu, D.

Michael, O. L. C.

Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007).
[Crossref]

Nannipieri, T.

Oak, S. M.

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Pachori, R. B.

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Park, J.

G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
[Crossref]

Park, N.

G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
[Crossref]

Paulose, V.

Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007).
[Crossref]

Raju, S. D. V. S. J.

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Ravindranath, S. V. G.

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Roncella, R.

Saxena, M. K.

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Shieh, W.

Shum, P. P.

Signorini, A.

Soto, M. A.

M. A. Soto, T. Nannipieri, A. Signorini, A. Lazzeri, F. Baronti, R. Roncella, G. Bolognini, and F. Di Pasquale, “Raman-based distributed temperature sensor with 1 m spatial resolution over 26 km SMF using low-repetition-rate cyclic pulse coding,” Opt. Lett. 36(13), 2557–2559 (2011).
[Crossref] [PubMed]

G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
[Crossref]

Sun, X.

X. Sun, J. Li, and M. J. Hines, “Distributed temperature measurement using a dual-core fiber with an integrated miniature turn-around,” Proc. SPIE 9852, 98520R (2016).
[Crossref]

Tang, M.

Tong, W.

Wang, M.

Wang, Y.

Wu, H.

Zhao, Z.

IEEE Trans. Inf. Theory (1)

D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41(3), 613–627 (1995).
[Crossref]

J. Lightwave Technol. (1)

Meas. Sci. Technol. (1)

G. Bolognini, J. Park, M. A. Soto, N. Park, and F. Di Pasquale, “Analysis of distributed temperature sensing based on Raman scattering using OTDR coding and discrete Raman amplification,” Meas. Sci. Technol. 18(10), 3211–3218 (2007).
[Crossref]

Opt. Commun. (1)

Y. Gong, O. L. C. Michael, J. Hao, and V. Paulose, “Extension of sensing distance in a ROTDR with an optimized fiber,” Opt. Commun. 280(1), 91–94 (2007).
[Crossref]

Opt. Express (1)

Opt. Laser Technol. (1)

M. K. Saxena, S. D. V. S. J. Raju, R. Arya, R. B. Pachori, S. V. G. Ravindranath, S. Kher, and S. M. Oak, “Raman optical fiber distributed temperature sensor using wavelet transform based simplified signal processing of Raman backscattered signals,” Opt. Laser Technol. 65(1), 14–24 (2015).
[Crossref]

Opt. Lett. (2)

Proc. SPIE (1)

X. Sun, J. Li, and M. J. Hines, “Distributed temperature measurement using a dual-core fiber with an integrated miniature turn-around,” Proc. SPIE 9852, 98520R (2016).
[Crossref]

Sensors (Basel) (1)

X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors (Basel) 12(7), 8601–8639 (2012).
[Crossref] [PubMed]

Other (5)

G. P. Agrawal, Nonlinear Fiber Optics, 3rd ed. (Academic, 2001).

“Technology Focus: Optical-fiber sensors,” Nat. Photonics2(3), 143–158 (2008).

A. Signorini, S. Faralli, M. A. Soto, G. Sacchi, F. Baronti, R. Barsacchi, A. Lazzeri, R. Roncella, G. Bolognini, and F. Di Pasquale, “40 km long-range Raman-based distributed temperature sensor with meter-scale spatial resolution,” in Optical Fiber Communication Conference, OSA Technical Digest (CD) (Optical Society of America, 2010), paper OWL2.
[Crossref]

D. A. Long, Raman Spectroscopy, (McGraw-Hill, 1977).

L. Thévenaz, “Review and progress on distributed fiber sensing,” in Optical Fiber Sensors, OSA Technical Digest (CD) (Optical Society of America, 2006), paper ThC1.

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Figures (9)

Fig. 1
Fig. 1 (a) Refractive index profile of the 2-mode FMF. (b) Refractive index profile of the 4-mode FMF.
Fig. 2
Fig. 2 Calculated H (P0, z) for different fibers as a function of P0 and z.
Fig. 3
Fig. 3 (a) H - P0 curves when z = 0; Pth1: SRS threshold of SSMF; Pth2: SRS threshold of 2-mode FMF; Pth3: SRS threshold of 4-mode FMF; Hmax1: maximum H value of SSMF; Hmax2: maximum H value of 2-mode FMF; Hmax3: maximum H value of 4-mode FMF. (b) H - P0 curves when z = 20km. (c) Ideal H - z curves when P0 = Pth. (d) H - z curves for three different fibers when P0 = Pth and splicing loss is considered.
Fig. 4
Fig. 4 Experimental setup of the RDTS system; WDM: wavelength division multiplex; APD: avalanche photo diode; DAQ: data acquisition; Inset.1: Transmittance spectra at WDM output; Inset.2: microscope image of taper spliced area.
Fig. 5
Fig. 5 (a) The measured far-field intensity distribution of 2-mode FMF. (b) The measured far-field intensity distribution of 4-mode FMF. (c) Electric field intensity profile of LP01 and LP11 in 2-mode FMF from simulation. (d) Electric field intensity profile of LP01, LP11, LP02 and LP21 in 4-mode FMF from simulation.
Fig. 6
Fig. 6 (a) Acquired backscattered stokes signal of different sensing fibers. (b) Acquired backscattered anti-stokes signal of different sensing fibers.
Fig. 7
Fig. 7 (a) Averaged AS traces without WT denoising. (b) SNR traces of the averaged AS traces without WT denoising. (c) WT de-noised AS traces. (d) SNR traces of the WT de-noised AS traces.
Fig. 8
Fig. 8 (a) Temperature-distance trace of SSMF. (b)Temperature-distance trace of the 2-mode FMF. (c) Temperature-distance trace of the 4-mode FMF. (d) RMS temperature resolutions for different fiber types.
Fig. 9
Fig. 9 (a) Spatial resolutions near 15.5 km of SSMF. (b) Spatial resolutions near 15.5 km of the 2-mode FMF. (c) Spatial resolutions near 15.5 km of the 4-mode FMF.

Tables (1)

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Table 1 Measured parameters of the fibers under test

Equations (5)

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R T P A S P S ( v a s v s ) 4 exp ( h Δ v k B T )
P A S ( z ) = D F A S ( z ) g R P 0 × G ( z ) × exp { 0 z [ α R ( ς ) + α A S ( ς ) ] d ς }
G ( z ) = exp ( g R P 0 ( z ) c Δ t / 2 n A e f f )
H ( P 0 , z ) g R P 0 × G ( z ) × exp { 0 z [ α R ( ς ) + α A S ( ς ) ] d ς } = g R P 0 × exp ( g R P 0 ( z ) c Δ t / 2 n A e f f ) × exp { 0 z [ α R ( ς ) + α A S ( ς ) ] d ς }
P t h 20 A e f f g R L e f f

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