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

噪音(视频) 相关系数 降噪 信号(编程语言) 算法 噪声测量 分辨率(逻辑) 物理 计算机科学 数学 人工智能 声学 统计 图像(数学) 程序设计语言
作者
Hanjie Liu,Ciming Zhou,Yandong Pang,Xi Chen,Dian Fan
出处
期刊:Journal of Lightwave Technology [Institute of Electrical and Electronics Engineers]
卷期号:42 (6): 2137-2143 被引量:1
标识
DOI:10.1109/jlt.2023.3325255
摘要

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.
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