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The accuracy of temperature measurement is often reduced due to random noise in Raman-based distributed temperature sensor (RDTS). A noise reduction method based on a nonlinear filter is thus proposed in this paper. Compared with the temperature demodulation results of raw signals, the proposed method in this paper can reduce the average maximum deviation of temperature measurement results from 4.1°C to 1.2°C at 40.0°C, 50.0°C and 60.0°C. And the proposed method in this paper can improve the accuracy of temperature measurement of Raman-based distributed temperature sensor better than the commonly used wavelet transform-based method. The advantages of the proposed method in improving the accuracy of temperature measurement for Raman-based distributed temperature sensor are quantitatively reflected in the maximum deviation and root mean square error of temperature measurement results. Therefore, this paper proposes an effective and feasible method to improve the accuracy of temperature measurement results for Raman-based distributed temperature sensor.

Raman-based distributed temperature sensor is widely used in power grid monitoring [

The accuracy of temperature measurement of RDTS is susceptible to the inherent loss of the sensing optical fiber and the random noise generated by different noise sources. The excessively high random noise greatly reduces the accuracy of temperature measurement of RDTS. In order to reduce the adverse effects of random noise on the accuracy of temperature measurement of RDTS, the researchers improved the signal-to-noise ratio (SNR) of the sensing signals by changing the hardware structure of RDTS [

In the research of using digital signal processing algorithms to improve the measurement accuracy of fiber-optics sensors, it is common to use wavelet transform (WT) algorithm to filter out random noise [

In Raman-based distributed fiber-optics temperature sensor, the semiconductor laser injects a certain power light pulse into the sensing fiber under the control of the synchronization pulse. Then, the temperature-modulated Raman backscattered light passes through the wavelength division multiplexer (WDM) to separate Stokes light and anti-Stokes light. And the two optical signals are converted into an electrical signal by Avalanche Photo Diode (APD) [

The intensity of Stokes light is one order of magnitude higher than the intensity of anti-Stokes light, and the sensitivity of these two lights to temperature is different. Anti-Stokes light is more sensitive to temperature changes, and Stokes light is often used as a reference for demodulating anti-Stokes light, which can effectively eliminate the effects of source jitter, fiber bending, and other factors. The ratio of the intensity of the anti-Stokes light to the intensity of the Stokes light has the following relationship with temperature [

F ( T ) = I a s I s = ( v a s v s ) 4 e − ( h c Δ γ T k B ) (1)

where I a s and I s are the intensity of anti-Stocks light and Stocks light, respectively; v a s and v s are the frequency of anti-Stocks light and Stocks light, respectively; h is the Planck constant, h = 6.626 × 10 − 34 J ⋅ s ; c is the speed of light in a vacuum, c = 3 × 10 8 m / s ; Δ γ is the number of waves; T is absolute temperature; k B is the Boltzmann constant, k B = 1.38 × 10 − 23 J ⋅ K − 1 .

The median filtering algorithm (MF) is a nonlinear filtering method [

y m = { M e d ( x m − n − 1 2 , x m − n − 1 2 + 1 , ⋯ , x k + n − 1 2 ) when n isodd M e d ( x m − n 2 , x m − n 2 + 1 , ⋯ , x m + n 2 − 1 ) when n iseven (2)

where n is the length of the neighborhood window. The process of processing the raw sensing signal by MF is as shown in

The selection of the appropriate window length is very important for the improvement of the accuracy of the MF algorithm to the measurement results of RDTS. In order to quantitatively evaluate the improvement effect of the different methods on the accuracy of the measurement results of RDTS, we measure the accuracy of the measurement results by the maximum deviation (MD) and the root mean square error (RMSE) [

M D = max | T o b s i − T a c t | i = 1 , 2 , ⋯ , N (3)

where T o b s i are the observed temperatures of the temperature abrupt zone measured by RDTS; T a c t is the actual temperature of the temperature abrupt zone measured by the mercury thermometer; N is the number of effective measurements of the temperature abrupt zone. The RMSE is expressed as

R M S E = 1 N ∑ i = 1 N ( T o b s i − T a c t ) 2 (4)

The experimental device is shown in

temperature water bath is set to 40.0˚C, 50.0˚C, and 60.0˚C, respectively. And the length of the sensing fiber placed in the constant temperature water bath (the length of the three hot zones) is 15 m, 20 m, and 30 m, respectively. The spacing between the hot zones is set to 15 m and 20 m, respectively. The sampling frequency of the data acquisition card is set to 100 MHz, and the number of time domain traces averaging is set to 10,000. The data processing software is MATLAB 2017a, Windows 10 Professional 64-bit, and the computer is configured as Intel i5-4200M, 2.5 GHz, 4 GB RAM, and 256 GB SSD.

The temperature information carried by the anti-Stokes signal and the Stokes signal obtained by the above experiment is demodulated by Equation (1), and the demodulation result of the raw data is shown in

As shown in

Specifically, the average maximum deviation of the temperature measurement results of the signals denoised by the median filtering algorithm with a window length of 10 (10-MF) is reduced from 4.1˚C to 2.6˚C at 40˚C, 50˚C, and 60˚C. The average maximum deviation of the temperature measurement results of the signals denoised by the median filtering algorithm with a window length of 15 (15-MF) is slightly increased from 4.1˚C to 4.4˚C at 40˚C, 50˚C, and 60˚C. The average maximum deviation of the temperature measurement results of the signals denoised by the median filtering algorithm with a window length of 20 (20-MF) is slightly increased from 4.1˚C to 1.4˚C at 40˚C, 50˚C, and 60˚C. The average

maximum deviation of the temperature measurement results of the signals denoised by the median filtering algorithm with a window length of 25 (25-MF) is slightly increased from 4.1˚C to 1.2˚C at 40˚C, 50˚C, and 60˚C.

As shown in

The maximum deviation of the temperature measurement is calculated by Equation (3). As shown in

The root mean square error of the temperature measurement is calculated by Equation (4). As shown in

Aiming at the problem of the adverse effect of random noise on temperature measurement accuracy in RDTS, we propose a noise reduction method based on nonlinear filtering of median filter to improve the accuracy of temperature measurement of RDTS. Compared with the demodulation results of Raw data, the method proposed in this paper can greatly improve the accuracy of temperature measurement of RDTS. Compared with the commonly used WT method, the method proposed in this paper can better improve the accuracy of temperature measurement of RDTS. The method proposed in this paper is better at both the maximum deviation and RMSE of temperature measurement results of RDTS, especially the median algorithm after selecting the appropriate window length. Therefore, this paper proposes an effective and available method to improve the accuracy of temperature measurement of RDTS. We think that further research on noise adaptive nonlinear filtering algorithms may provide a potential solution to the problem of how to select the appropriate window length for the RDTS sensing signals with different SNR.

This work was supported by the funds of Science Plans of Sichuan Province, China under Grant 2018SZ0347 and Grant 17CZ0004.

The authors declare no conflicts of interest regarding the publication of this paper.

Wang, X., Liu, T. and Wang, H.-H. ^{}(2019) Research on Noise Reduction Approach of Raman-Based Distributed Temperature Sensor Based on Nonlinear Filter. Open Journal of Applied Sciences, 9, 631-639. https://doi.org/10.4236/ojapps.2019.98051