Improved wavelet modulus maximum method for distributed optical fiber temperature sensing

Abstract

The temperature measurement accuracy of the Raman distributed optical fiber temperature sensing system is an important metric. Aimed at the reduction of the temperature measurement error, which is caused by the noises in the sensor system, an improved wavelet modulus maximum method, which combine the 3sigm criterion with the traditional wavelet modulus maximum algorithm is proposed. In this proposed algorithm, the 3sigm value is used as the threshold for the highest decomposition layer of the wavelet modulus maximum method to judge whether it is a signal or noise, instead of using the ratio of the modulus maximum of the highest decomposition level to the number of decomposition levels to determine the threshold. We conduct distributed temperature measurement on a 10-km long multimode fiber, and analyze the experimental data by using the improved algorithm. The results show that the temperature measurement fluctuation obtained by improved method is 0.497°C on average. It is better than the 1.13°C that is obtained by the traditional wavelet modulus maximum algorithm. Compared with the traditional algorithms, the 3sigm criterion algorithm has improved the temperature measurement accuracy significantly. The improved algorithm provides a new idea for the selection of the threshold value, which makes the threshold selection more statistically.

Wavelet Decomposition Layer Selection for the φ-OTDR Signal

“…Wavelet transform (WT) stands as a widely employed technique for denoising, filtering, and compressing signals in distributed fiber optic sensing systems, contributing to the enhancement of various sensing technologies, such as Brillouin optical time-domain analyzer (BOTDA), Brillouin optical time-domain reflectometry (BOTDR), Raman optical time-domain reflectometry (ROTDR), phase-sensitive optical time-domain reflection (φ-OTDR), and optical frequency-domain reflectometer (OFDR), among others technologies [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Its synergy with artificial intelligence methods also helps in time-frequency feature analysis and facilitates feature extraction [15][16][17][18][19][20][21][22].…”