Novel Method for Improving Temperature Resolution of Fiber Optic Sensor Based on Variational Mode Decomposition

The temperature resolution of Fiber Optic Sensor is greatly limited by background noise. In this paper, we propose a noise suppression method, based on variational mode decomposition (VMD), to enhance the temperature resolution. The VMD algorithm is employed to decompose the original signal into several intrinsic mode function (IMF) components, each limited by a certain bandwidth. We calculate the correlation coefficient between each IMF component and the original sensing signal, and filter out IMFs with correlation coefficients less than the average, which primarily contain noise signal. The IMFs with correlation coefficients greater than the average are retained for signal reconstruction. The experimental and comparative results demonstrate the exceptional performance of the proposed algorithm in denoising temperature signals, effectively removing background and environmental noise while preserving temperature information. Furthermore, by analyzing the power spectrum of the temperature signal before and after noise suppression, we observe that the proposed algorithm can achieve a temperature resolution of 10 −6 °C, which is two orders of magnitude higher than that before noise suppression (10 −4 °C). These results validate the efficacy of our method in reducing system noise and improving the accuracy and reliability of interferometric temperature sensors.