Laser & Optoelectronics Progress, Volume. 59, Issue 7, 0706004(2022)
Research and Application of Sample Entropy Feature Extraction Based on Local Mean Decomposition
Distributed optical fiber sensing technology has been widely used in the safety monitoring of pipeline transportation. Accurately extracting and classifying the characteristics of different optical fiber vibration signals is a research hotspot in recent years. Aiming at the defects that the traditional time-frequency analysis method needs to manually set the basis function and cannot eliminate the interference of high-frequency noise when studying the optical fiber vibration signal, the local mean decomposition (LMD) method with adaptive characteristics is used to process the signal, and a new method based on LMD is proposed in this paper. Decomposed feature extraction and recognition methods. First, LMD is performed on the original signal to obtain several product function components; then, the signal is reconstructed by the principle of autocorrelation, and the sample entropy features and energy features of the reconstructed signal are extracted; finally, the above features are fused and sent to the support vector machine for training recognition. The experimental results show that the method can effectively identify different vibration types, and has a high accuracy rate.
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Hongquan Qu, Xiang Ji, Zhiyong Sheng, Hongbin Qu, Ling Wang. Research and Application of Sample Entropy Feature Extraction Based on Local Mean Decomposition[J]. Laser & Optoelectronics Progress, 2022, 59(7): 0706004
Category: Fiber Optics and Optical Communications
Received: May. 26, 2021
Accepted: Jun. 28, 2021
Published Online: Mar. 8, 2022
The Author Email: Qu Hongquan (qhqphd@ncut.edu.cn)