1. Introduction
Fiber optic sensors are important in smart structures, mostly as a transmission line, as a sensing medium, or a combination of both. Fiber Bragg grating (FBG) is a favorable fiber optic sensor to measure strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. In addition, applications of FBG sensors related to bridge monitoring, damage detection, structural health monitoring, railway transportation applications, and other harsh environment applications [1–3]. FBG sensors have the advantages of being immune to electromagnetic interference, light weight, small size, highly accurate, longer stability, and capable in measuring ultra-high-speed events. The sensing characteristics of FBG sensors involve identifying the variation of the central Bragg wavelength when it is subjected to physical parameters like vibration, strain, temperature, and other changes in the properties of the sensor [4–6]. Multiplexing of FBG sensors is an effective technology to deliver multipoint measurement along a single-fiber cable. Multiplexing reduces the operation and installation costs for quasidistributive strain measurements [7]. Among the various multiplexing techniques, wavelength division multiplexing (WDM) is an essential mechanism to recognize the exact central wavelength of each FBG sensor within the sensor network [7, 8]. In WDM, each FBG sensor needs a different Bragg wavelength and no spectral overlap is allowed. However, the number of multiplexed sensors is restricted by both the broadband source bandwidth and the operating wavelength range required for each FBG sensor [9].
Recently, researchers have proposed intensity and wavelength division multiplexing (IWDM) techniques to increase the number of FBG sensors to be multiplexed [8–10]. IWDM has the advantage of low complexity. The IWDM scheme proposed in [9] used tunable fiber ring laser as a light source and a Fabry–Perot filter to select different wavelengths. However, this scheme is inappropriate for long-distance environment sensing, as it does not amplify the FBG signal. In [10], the authors proposed capacity and capability enhancement of the FBG sensor system using IWDM technique. However, IWDM has a problem of unmeasurable gap when two or more FBG spectra are partially or fully overlapped. Overlapping spectra cause crosstalk among the sensors and induce errors in central Bragg wavelength detection. Moreover, IWDM have a restriction on long-distance transmission when the number of FBG sensors increases. Nowadays, in addition to increasing the number of FBG sensors using IWDM, increasing the signal transmission distance is a very important issue.
In FBG sensor systems, the maximum signal transmission distance is commonly limited to 25 km due to signal loss in fiber link and Rayleigh scattering [11–16]. Moreover, signal loss and Rayleigh scattering induce optical noise and degrade the transmitted signal quality. Signal amplification is very important for long-distance transmission and remote sensing functionality. Among different optical signal amplification techniques, Raman amplification is the best amplifier to increase the transmission distance. Raman amplification entails generating a gain medium within the transmission fiber, which amplifies the signal before it reaches the optical receiver. Raman amplification has the capacity to amplify signals in any wavelength band using an appropriate pumping scheme [13–15]. By adding a Raman amplifier to a fiber span, signal power loss is decreased and it highly improves the signal-spontaneous beat noise performance. In order to realize a long-distance FBG sensor system, several approaches are proposed [13–17]. In [13], Raman laser for long-distance sensing has been demonstrated. However, FBGs are grouped together in the direction of the end of a long fiber, and consequently, the dynamic range of the interrogator is not possible to extend as it affects the response time of the system. Scheme [17] proposed linear-cavity fiber Raman laser for a long-distance FBG sensor system. The cavity consists of two FBGs connected to a fiber loop mirror. However, a fiber loop mirror has a limiting factor in system stability and configuration complexity. In addition, when strain is applied, the spectra of two FBGs might be overlapped and cannot address the sensing information of each FBG. Recently, besides increasing the number of FBG sensors and increasing the maximum transmission distance, much attention was focused on wavelength detection of spectrally overlapped FBG sensors.
In IWDM, central Bragg wavelength detection technique is a very important issue to detect each FBG central wavelength even when two or more FBG spectra are partially or fully overlapped [18]. Previous researchers have proposed several central wavelength detection methods, such as tabu-gradient search algorithm [18], filters [19, 20], conventional peak detection (CPD) [21], radial basis function network [22], and ADALINE network [23]. In [19], the sensing accuracy of FBG sensors was improved using a digital low-pass filter. However, the filter affects the measurement accuracy due to noise within the filter. CPD uses a tunable optical filter to detect the central wavelength of each FBG sensor [21]. However, this technique is inappropriate if the spectra of two or more FBGs are overlapped because it cannot address the sensing information of each FBG. All the above central wavelength detection methods have limitations on increasing the number of FBG sensors and detecting the central Bragg wavelength of FBGs when FBG spectra are overlapped.
This paper proposes a novel extreme learning machine (ELM) based central Bragg wavelength detection scheme for spectrally overlapped FBG sensors. An ELM accurately determines the central Bragg wavelengths. Compared to CPD machine learning methods, such as a neural network (NN) or support vector machine (SVM) [24], an ELM has the importance of good generalization and fast learning and requires less human intervention. An ELM is effective in real-time applications because it automatically determines all network parameters [25, 26]. Moreover, we propose IWDM to increase the number of FBG sensors and we employ a Raman amplifier to increase the sensing signal transmission distance and to improve the signal quality.
The rest of this paper is organized as follows. In Section 2, a brief discussion about operational principles with a mathematical expression is presented. Section 3 presents about an ELM. In Section 4, the experimental and simulation results are presented. Finally, we summarize the conclusion part of this paper in Section 5.
2. Operational Principles and Methods
The schematic diagram of applying IWDM and ELM techniques to a FBG sensor system is explained by using three FBG sensors as shown in Figure 1. A broadband light source is emitted from EDFA. The light emitted from the broadband light sources is injected into FBG sensors through a 2 × 2 coupler (50 : 50) and split into two branches (50 : 50). The one branch of a 50 : 50 coupler comprised a 1 × 2 coupler (30 : 70). The two FBG sensors (i.e., FBG1 and FBG2) are employed after a 30% and 70% output power ratio of a 1 × 2 coupler, respectively. FBG3 is employed after a 50% output power ratio of a 50 : 50 coupler. FBG3 is deployed after a 45 km SMF in order to have a gain medium. The back-reflected light from each FBG is propagated through the other arm of a 2 × 2 coupler and passes into the Raman amplifier. The Raman output power can be pumped to the SMF via a WDM coupler. The Raman amplifier consists of two laser diodes (LDs) with wavelengths of 1420 nm and 1450 nm. The polarization beam combiner (PBC) combines the outputs from the two LDs. The combined outputs from the two laser diodes are inserted into the Raman pump through the WDM coupler and the combined light is fed into a 45 km SMF. The output signal from the Raman amplifier is fed into the optical spectrum analyzer (OSA) to show the back-reflected wavelengths and to measure the central Bragg wavelengths. Finally, the measured reflected spectra of FBGs from OSA are sent to a personal computer (PC) for additional data processing.
[figure omitted; refer to PDF]The traditional WDM requires that each FBG sensor should have a unique spectral region, which limits the multiplexing capability of the sensor system [9]. The proposed method allows the reflection spectra of two FBGs which are overlapped when entering into the overlapping region. When a bandwidth of the full width at half maximum (FHWM) is larger than the distance between two FBG central wavelengths, it is difficult to identify each FBG central Bragg wavelength [24]. Hence, in our study, the central wavelength detection of overlapped spectra is converted to a regression problem. We assume that
If the spectra of FBGs are overlapped, it is very difficult to determine the central Bragg wavelengths (
During the testing phase, only the new measured FBG reflection spectra from the OSA are fed into the well-trained ELM model. The well-trained ELM model quickly detects (identify) the central Bragg wavelengths of each FBG.
3. Extreme Learning Machine (ELM)
This paper proposes an ELM-based central Bragg wavelength detection to address the issue of a nonlinear relationship between FBG and strain factors. Scheme [24] proposed an ELM for single-hidden layer feedforward networks (SLFNs). A SLFN structure contains three layers, such as the input layer, hidden layer, and output layer, which are connected by neurons, as shown in Figure 3.This method is limited to feedforward neural networks with a single nonlinear hidden layer, as this type of network is capable of making a random close approximation of any continuous nonlinear mapping [27]. Instead of using traditional gradient-based learning methods which require several iterations, an ELM has its input weights randomly generated and uses simple matrix computations between hidden layers and output layers to determine the output weights. Solving the regularized least squares in ELM is faster than solving the quadratic programming problem in the SVM method [25, 26].
[figure omitted; refer to PDF]The ELM approach considers the wavelength detection problem as a regression problem. During the training process, the ELM randomly generates input weights
According to [24], (3) can be rewritten into a matrix form as follows:
(1)
Determine the number of hidden layer neurons, randomly set bias
(2)
Choose distinguishable activation function and then calculate output matrix
(3)
The weights
4. Experimental and Simulation Results
Figure 1 shows the experimental setup of the IWDM-FBG sensor system using a Raman amplifier and ELM. A broadband light source emitted from EDFA is injected into three FBG sensors. The measured reflection spectra of FBGs are sampled into 1001 points within a wavelength range from 1545.71 nm to 1550.71 nm. The span width of OSA is set to be 4 nm and the minimum resolution of the OSA is 0.03 nm. The measured reflection spectra of FBGs from the OSA are passed to a PC for further data processing. The central wavelengths of FBG1, FBG2, and FBG3 are 1547.54 nm, 1547.83 nm, and 1548.21 nm, respectively. The 3 dB bandwidths of the FBG1, FBG2, and FBG3 sensors are 0.24 nm, 0.226 nm and 0.24 nm, respectively. The reflectivity as well as bandwidth of the FBG sensors changes under different grating lengths and refractive index changes. We assumed that the reflection spectra of three FBGs are Gaussian shaped given by
In the FBG sensor system, the maximum signal transmission distance is commonly limited to 25 km due to signal loss in fiber link and Rayleigh scattering. Signal loss and Rayleigh scattering induces optical noise and degrade the transmitted signal quality [11–16]. Therefore, we use a Raman amplifier to increase the transmission distance (long-distance sensing) and to compensate for the signal losses in long-distance transmission. As shown in Figure 1, FBG3 is deployed after 45 km SMF for long-distance signal transmission or for remote sensing up to 45 km. Figure 4(a) shows the measured spectra of three FBGs when the Raman pump is turned off. As shown in the figure, the power of FBG3 wavelength is very small and even disappears when the Raman pump is turned off. Hence, after 45 km transmission, it is very challenging to detect the central Bragg wavelength of FBG3 without employing a Raman amplifier. Therefore, by adding a Raman amplifier to a fiber span (FBG3), signal power loss is decreased and it highly improves the signal-spontaneous beat noise performance. Signal propagating along the fiber will be attenuated, but as it moves toward the fiber end where the Raman pump is located, it will start to experience some gain from the Raman pump wavelength. The higher power in the signal thus increases the signal-to-noise ratio (SNR), which enables longer fiber span, higher capacity and spectral efficiency, and longer distance. On the other hand, FBG1 and FBG2 have not been positioned in long distance compared to FBG3 as shown in Figure 1. Hence, FBG1 and FBG2 can sense signals below 25 km. As shown in Figure 4(a), the measured spectra of FBG1 and FBG2 are visible and can detect the central Bragg wavelength even when the Raman pump is turned off. Because of this, we do not need any Raman amplification in the FBG1 and FBG2 sensors. Figure 4(b) shows the measured spectra when the Raman pump is turned on. As shown in the figure, the sensing signal quality of FBG1 and FBG2 is the same with when the Raman pump is turned off. But, the sensing signal of FBG3 is significantly improved when the Raman pump is turned on because the Raman pump compensates for the signal loss of a long-distance fiber. After 45 km SMF transmission, the gain medium value of FBG3 before and after the Raman amplifier is 4 dB, which is adequate to compensate for the signal losses.
[figures omitted; refer to PDF]
IWDM technique can increase the number of FBG sensors to be multiplexed by keeping the same dynamic range for each FBG sensor [8]. However, as the number of FBG sensors increases, the spectra of two or more FBGs are partially or fully overlapped. Hence, we use ELM to accurately determine the central Bragg wavelengths of each FGB when the spectra of two FBGs are overlapped. We develop a simulation environment using MATLAB to verify ELM-based central Bragg wavelength detection techniques when the spectra of FBGs are overlapped. The simulation runs on a PC, which has Intel Core i7-4790 3.60 GHz CPU and 20.48 GB RAM. The simulation of ELM-based central wavelength detection is conducted with three FBG sensors. Assume that the reflected spectra of three FBGs are Gaussian shaped [30]. The reflected spectra of three FBGs are added with random noise using (1) to simulate in a bad environment. During the simulation, the central Bragg wavelength of FBG1 is shifted from 1547.54 nm to 1548.662 nm by applying a changing strain to FBG1, while the Bragg wavelengths of FBG2 and FBG3 are fixed at 1547.83 nm and 1548.21 nm, respectively. The strain applied to FBG1 is increased by ~75 μɛ at each step. If a strain is applied to FBG1, the central Bragg wavelength will shift because the refractive index changes. Hence, the detected wavelength of FBG1 also shifts. The center Bragg wavelength shift of FBG1 according to the axial strain applied to a fiber grating is described as
Therefore, we can change the central wavelength of FBG1 at each applied strain value using (1) and then the reflection spectra of FBGs are calculated by (1) using MATLAB simulation. The measurements are repeated for 14 different strain values (steps). Figure 5 shows the reflected spectra of three FBGs when a strain is applied to FBG1 corresponding to strain steps 0, 2, 4, 6, 8, 10, 12, and 14. As shown in the figure, it can be seen that the spectra of three FBGs are distinct at strain step 0, step 12, and step 14. But, the spectra of FBG1 and FBG2 are partially overlapped at strain step 2 and fully overlapped at strain step 4. Similarly, the spectra of FBG1 and FBG3 are partially overlapped at strain step 6 and step 10 and fully overlapped at strain step 8. As shown in Figure 6, the overlapping FBG spectra causes crosstalk and unmeasurable gaps between FBG sensors, which is why it is difficult to determine the exact central (peak) wavelength using the traditional CPD technique (without using ELM techniques).
[figure omitted; refer to PDF] [figure omitted; refer to PDF]Figure 6 shows the unmeasurable gaps of three FBG sensors when the spectra of two FBGs are overlapped. We apply a strain within the range from 0 με to 1052 με to FBG1, and this causes its central wavelength to shift from 1547.54 nm to 1548.662 nm. When the applied strain to FBG1 is between 150 με and 450 με, the spectra of FBG1 and FBG2 are overlapped and we cannot detect the central wavelengths of the two FBG sensors using CPD technique. The range that cannot detect the central wavelength of FBGs is called unmeasurable gaps. When the applied strain to FBG1 is greater than 450 με, the spectra of FBG1 and FBG2 are separated but the spectra of FBG1 are overlapped with FBG3. Applying a strain to FBG1 within the range of 450 με–900 με will create unmeasurable gaps for both FBG1 and FBG3. Hence, we cannot detect the central wavelengths of FBG1 and FBG3 using CPD techniques. We can directly detect all the three FBG central Bragg wavelengths using CPD technique only if the applied strain is less than 150 με or greater than 900 με. When the applied strain is between 150 με and 900 με, unmeasurable gaps will be created for at least in the two FBG sensors. Because of this, we have proposed ELM to solve the overlapping or unmeasurable gap problem subsequently. For each applied strain value, the well-trained ELM can accurately detect the central Bragg wavelengths of three FBGs using (8) even when the spectra of FBGs are within the overlapping or unmeasurable gap range.
The proposed ELM techniques is demonstrated and tested by using MATLAB simulation to verify the feasibility of an ELM for wavelength detection mechanisms. In order to train an ELM, the training dataset are generated using (1), which is the reflection spectra of three FBGs by applying a changing strain to FBG1. The measurements are repeated for 14 different strain values (steps), and the reflection spectra of FBGs are calculated by (1) using MATLAB simulation. Figure 5 shows the reflection spectra of three FBGs when we apply different strains to FBG1.The training dataset has inputs and corresponding targets. The inputs are the reflection spectra of three FBGs at each applied strain value to FBG1, and the corresponding targets are the central wavelength of each FBG at each applied strain value to FBG1. The generated training data can be preprocessed and prepared to be used for training an ELM based on (2). The sample size of training data is 15000. ELM is trained for 30 times with the training datasets using feedforward neural network algorithms. During the training phase, we have adjusted the number of neurons, number of epochs, and the number of hidden layers to get the best generalization performance of the ELM. The best performance of the well-trained ELM is obtained when the number of neurons is 2000 and the number of epochs is 1000. The training and testing accuracy increases when the number of neurons increases. The test dataset is generated from the reflection spectra of three FBGs measured by an OSA using an experimental method. During the experiment, strain is applied to FBG1 to shift the central Bragg wavelength from 1547.54 nm to 1548.662 nm, while the central Bragg wavelengths of FBG2 and FBG3 are fixed. To apply a strain to FBG1, FBG1 is mounted on a translation stage (TS). Then, by manually tuning the TS, a strain is applied to FBG1. This manual tuning of the TS in the fiber grating leads to a shift in the central Bragg wavelength of FBG1. When the central Bragg wavelength of FBG1 shifts, an overlap occurs between FBG1 and FBG2 and between FBG1 and FBG3 at a different time. The shift in the central Bragg wavelength of FBG1 is observed and obtained from an OSA. The applied strain to FBG1 at each step is ~75 μɛ. The strain (
[figures omitted; refer to PDF]
Figures 7(a) and 7(b) show the reflected spectra of three FBGs measured by an OSA by applying a strain to FBG1 corresponding to strain steps 4 and step 8, respectively. As shown in the figure, the two FBG sensor spectra are overlapped, which is why it is difficult to identify the central Bragg wavelength of each FBG using traditional CPD techniques. Therefore, we use ELM techniques to solve this problem. For each applied strain, an ELM achieved to obtain the exact central Bragg wavelengths of FBG1, FBG2, and FBG3 in all independent test runs. Figure 8 shows the simulation output of the detected central Bragg wavelengths of three FBGs using the well-trained ELM model when the spectra of FBG1 and FBG2 are fully overlapped at strain step 4 (see Figure 7(a)). The absolute value of the difference between the predicted central wavelength value and actual central wavelength values is calculated as the detection error, expressed as detection error = |predicted central wavelength value − actual central wavelength values|. The detection error of the proposed ELM model when FBG1 and FBG2 spectra are overlapped is 0.018, which is a very small error. This indicates that we can accurately identify the central Bragg wavelengths of FBG1 and FBG2 even though the input spectra of the two FBG sensors are fully overlapped. Similarly, Figure 9 shows the simulation output of the detected central Bragg wavelengths of three FBGs using the well-trained ELM when the spectra of FBG1 and FBG3 are fully overlapped at strain step 8 (see Figure 7(b)). The detection error of the proposed method when FBG1 and FBG3 spectra are overlapped is 0.021, which is a very small error. Because the central wavelengths of FBG2 and FBG3 are fixed (constant), we can easily identify which central wavelength is for FBG1, FBG2, and FBG3 when the well-trained ELM detects (identify) the central wavelengths of three FBGs (see Figures 8 or 9). Only the FBG1 central wavelength varies during wavelength shift or overlap. Therefore, Figures 8 and 9 indicate that the well-trained ELM model can accurately detect the central Bragg wavelengths of FBG1, FBG2, and FBG3 even though the input spectra of the two FBG sensors are fully overlapped.
[figure omitted; refer to PDF] [figure omitted; refer to PDF]Figure 10 shows the output of the well-trained ELM model, which is the detected central Bragg wavelengths of three FBGs at each strain value (steps). When the central Bragg wavelengths of two FBGs are partially or fully overlapped (from strain step 2 to step 12), the proposed ELM can determine the central Bragg wavelengths of three FBGs even in the overlapping wavelength range. The performance of the ELM model can be described in terms of training time, testing time, and root-mean-square error (RMSE) values of the testing result. An ELM has an enormous advantage in training time and learning speed. When the number of hidden neurons is 2000, the training time of the ELM is 12 s and the testing time of the ELM is 0.10 s. Both the training time and testing time increase when the number of hidden neurons increases. Since an ELM model can directly determine the central Bragg wavelengths of FBG sensors, an ELM is much simpler and greatly reduces the training time and testing time than CPD techniques. RMSE can evaluate the central Bragg wavelength detection performance of the well-trained ELM when two or more FBG spectra are partially or fully overlapped. RMSE values can be calculated by the following equation:
Figure 11 shows the well-trained ELM central Bragg wavelength detection errors (i.e.,
5. Conclusion
This paper introduces an IWDM-FBG sensor system using a Raman amplifier and ELM. FBGs are favorable sensors in different applications and have the advantages of being immune to electromagnetic interference, light weight, flexible, stretchable, small size, highly accurate, longer stability, and capable in measuring ultra-high-speed events. A Raman amplifier is used to increase the sensing signal transmission distance and optimize the quality of the signal. Increasing the Raman pump power increases the transmission distance and the sensing signal quality. The experimental result revealed that a Raman amplifier can compensate for the signal losses and can provide a stable sensing output even beyond a 45 km transmission distance. Moreover, we achieve a remote sensing of strain measurement at a remote location of 45 km. We emplo
Conflicts of Interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Acknowledgments
This work was supported by the Ministry of Science and Technology, Taiwan, under Contract MOST 106-2221-E-027-100.
[1] R. Kashyap, Fiber Bragg Gratings, 2009.
[2] A. D. Kersey, M. A. Davis, H. J. Patrick, M. LeBlanc, K. P. Koo, C. G. Askins, M. A. Putnam, E. J. Friebele, "Fiber grating sensors," Journal of Lightwave Technology, vol. 15 no. 8, pp. 1442-1463, DOI: 10.1109/50.618377, 1997.
[3] J. Cui, Y. Hu, K. Feng, J. Li, J. Tan, "FBG interrogation method with high resolution and response speed based on a reflective-matched FBG scheme," Sensors, vol. 15 no. 7, pp. 16516-16535, DOI: 10.3390/s150716516, 2015.
[4] H. F. Lima, P. F. Antunes, J. D. L. Pinto, R. N. Nogueira, "Simultaneous measurement of strain and temperature with a single fiber Bragg grating written in a tapered optical fiber," IEEE Sensors Journal, vol. 10 no. 2, pp. 269-273, DOI: 10.1109/JSEN.2009.2030261, 2010.
[5] C. C. Ma, C. W. Wang, "Transient strain measurements of a suspended cable under impact loadings using fiber Bragg grating sensors," IEEE Sensors Journal, vol. 9 no. 12, pp. 1998-2007, DOI: 10.1109/JSEN.2009.2031327, 2009.
[6] Z. Zhang, L. Yan, W. Pan, B. Luo, P. Wang, L. Guo, W. Zhou, "Sensitivity enhancement of strain sensing utilizing a differential pair of fiber Bragg gratings," Sensors, vol. 12 no. 4, pp. 3891-3900, DOI: 10.3390/s120403891, 2012.
[7] K. S. Choi, J. Youn, E. You, J. A. Yoon, G. A. Kim, S. J. Baik, K. Im, K. T. Kim, S. H. Jeong, "Improved spectral tag method for FBG sensor multiplexing with equally spaced spectral codes and simulated annealing algorithm," 2009 IEEE Sensors, pp. 1256-1259, DOI: 10.1109/ICSENS.2009.5398380, .
[8] L. Zhang, Y. Liu, J. A. R. Williams, I. Bennion, "Enhanced FBG strain sensing multiplexing capacity using combination of intensity and wavelength dual-coding technique," IEEE Photonics Technology Letters, vol. 11 no. 12, pp. 1638-1640, DOI: 10.1109/68.806872, 1999.
[9] P. C. Peng, J. H. Lin, H. Y. Tseng, S. Chi, "Intensity and wavelength-division multiplexing FBG sensor system using a tunable multiport fiber ring laser," IEEE Photonics Technology Letters, vol. 16 no. 1, pp. 230-232, DOI: 10.1109/LPT.2003.818916, 2004.
[10] C. H. Yeh, Y. H. Zhuang, N. Tsai, C. W. Chow, "Capacity and capability enhancements of FBG sensor system by utilizing intensity and WDM detection technique," Smart Materials and Structures, vol. 26 no. 3, article 035026,DOI: 10.1088/1361-665X/aa5747, 2017.
[11] H. Y. Fu, H. L. Liu, W. H. Chung, H. Y. Tam, "A novel fiber Bragg grating sensor configuration for long-distance quasi-distributed measurement," IEEE Sensors Journal, vol. 8 no. 9, pp. 1598-1602, DOI: 10.1109/JSEN.2008.929082, 2008.
[12] S. K. Liaw, Y. W. Lee, H. W. Huang, W. F. Wu, "Multiwavelength linear-cavity SOA-based laser array design for multiparameter and long-haul sensing," IEEE Sensors Journal, vol. 15 no. 6, pp. 3353-3358, DOI: 10.1109/JSEN.2015.2390674, 2015.
[13] Y. G. Han, T. V. A. Tran, S. H. Kim, S. B. Lee, "Multiwavelength Raman-fiber-laser-based long-distance remote sensor for simultaneous measurement of strain and temperature," Optics Letters, vol. 30 no. 11, pp. 1282-1284, DOI: 10.1364/OL.30.001282, 2005.
[14] Y. G. Han, T. V. A. Tran, S. H. Kim, S. B. Lee, "Development of a multiwavelength Raman fiber laser based on phase-shifted fiber Bragg gratings for long-distance remote-sensing applications," Optics Letters, vol. 30 no. 10, pp. 1114-1116, DOI: 10.1364/OL.30.001114, 2005.
[15] Y. Nakajima, Y. Shindo, T. Yoshikawa, "Novel concept as long-distance transmission FBG sensor system using distributed Raman amplifier," Proceedings of the 16th International Conference on Optical Fiber Sensor (OFS) Th1-4, pp. 530-533, .
[16] B. Y. Guo, W. C. Tang, Y. C. Manie, M. A. Bitew, H. K. Lu, P. C. Peng, "Long-distance sensing fiber sensor system using broadband source and Raman amplifier," 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), pp. 413-414, DOI: 10.1109/ICCE-China.2017.7991171, .
[17] P. C. Peng, H. Y. Tseng, S. Chi, "Long-distance FBG sensor system using a linear-cavity fiber Raman laser scheme," IEEE Photonics Technology Letters, vol. 16 no. 2, pp. 575-577, DOI: 10.1109/LPT.2003.823128, 2004.
[18] X. Xu, J. Chicharo, J. Xi, "Improving the performance of IWDM FBG sensing system using tabu-gradient search algorithm," 2006 International Symposium on Intelligent Signal Processing and Communications, pp. 788-791, DOI: 10.1109/ISPACS.2006.364767, .
[19] C. C. Chan, W. Jin, M. S. Demokan, "Enhancement of measurement accuracy in fiber Bragg grating sensors by using digital signal processing," Optics & Laser Technology, vol. 31 no. 4, pp. 299-307, DOI: 10.1016/S0030-3992(99)00071-7, 1999.
[20] C. C. Chan, J. M. Gong, C. Z. Shi, W. Jin, M. Zhang, L. M. Zhou, M. S. Demokan, "Improving measurement accuracy of fiber Bragg grating sensor using digital matched filter," Sensors and Actuators A: Physical, vol. 104 no. 1, pp. 19-24, DOI: 10.1016/S0924-4247(02)00437-5, 2003.
[21] E. Udd, "Fiber optic smart structures," Proceedings of the IEEE, vol. 84 no. 6, pp. 884-894, DOI: 10.1109/5.503144, 1996.
[22] A. S. Paterno, J. C. C. Silva, M. S. Milczewski, L. V. R. Arruda, H. J. Kalinowski, "Radial-basis function network for the approximation of FBG sensor spectra with distorted peaks," Measurement Science and Technology, vol. 17 no. 5, pp. 1039-1045, DOI: 10.1088/0957-0233/17/5/S17, 2006.
[23] C. C. Chan, C. Z. Shi, W. Jin, D. N. Wang, "Improving the wavelength detection accuracy of FBG sensors using an ADALINE network," IEEE Photonics Technology Letters, vol. 15 no. 8, pp. 1126-1128, DOI: 10.1109/LPT.2003.815360, 2003.
[24] G. B. Huang, Q. Y. Zhu, C. K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70 no. 1–3, pp. 489-501, DOI: 10.1016/j.neucom.2005.12.126, 2006.
[25] X. Chen, Z. Y. Dong, K. Meng, Y. Xu, K. P. Wong, H. W. Ngan, "Electricity price forecasting with extreme learning machine and bootstrapping," IEEE Transactions on Power Systems, vol. 27 no. 4, pp. 2055-2062, DOI: 10.1109/TPWRS.2012.2190627, 2012.
[26] R. Rajesh, J. S. Prakash, "Extreme learning machines-a review and state-of-the-art," International Journal of Wisdom Based Computing, vol. 1 no. 1, pp. 35-39, 2011.
[27] F. Han, D. S. Huang, Z. H. Zhu, T. H. Rong, "The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine," International Journal of Neural Systems, vol. 16 no. 1, pp. 39-46, DOI: 10.1142/S0129065706000494, 2006.
[28] M. Pal, "Extreme-learning-machine-based land cover classification," International Journal of Remote Sensing, vol. 30 no. 14, pp. 3835-3841, DOI: 10.1080/01431160902788636, 2009.
[29] D. Serre, Matrices: Theory and Applications,DOI: 10.1007/978-1-4419-7683-3, 2010.
[30] C. Z. Shi, N. Zeng, C. C. Chan, Y. B. Liao, W. Jin, L. Zhang, "Improving the performance of FBG sensors in a WDM network using a simulated annealing technique," IEEE Photonics Technology Letters, vol. 16 no. 1, pp. 227-229, DOI: 10.1109/LPT.2003.820105, 2004.
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Copyright © 2018 Yibeltal Chanie Manie et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0/
Abstract
A fiber Bragg grating (FBG) sensor is a favorable sensor in measuring strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. FBG sensors have the following advantages: immune to electromagnetic interference, light weight, small size, flexible, stretchable, highly accurate, longer stability, and capable in measuring ultra-high-speed events. In this paper, we propose and demonstrate an intensity and wavelength division multiplexing (IWDM) FBG sensor system using a Raman amplifier and extreme learning machine (ELM). We use an IWDM technique to increase the number of FBG sensors. As the number of FBG sensors increases and the spectra of two or more FBGs are overlapped, a conventional peak detection (CPD) method is unappropriate to detect the central Bragg wavelength of each FBG sensor. To solve this problem, we use ELM techniques. An ELM is used to accurately detect the central Bragg wavelength of each FBG sensor even when the spectra of FBGs are partially or fully overlapped. Moreover, a Raman amplifier is added to a fiber span to generate a gain medium within the transmission fiber, which amplifies the signal and compensates for the signal losses. The transmission distance and the sensing signal quality increase when the Raman pump power increases. The experimental results revealed that a Raman amplifier compensates for the signal losses and provides a stable sensing output even beyond a 45 km transmission distance. We achieve a remote sensing of strain measurement using a 45 km single-mode fiber (SMF). Furthermore, the well-trained ELM wavelength detection methods accurately detect the central Bragg wavelengths of FBG sensors when the two FBG spectra are fully overlapped.
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1 Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei, Taiwan
2 Department of Computer Science, Bahir Dar University, Bahir Dar, Ethiopia