基于FDM能量熵的特征提取方法及其在光纤振动识别中的应用  

Feature Extraction Method Based on FDM Energy Entropy and its Application on Optical Fiber Vibration Recognition

作　　者：曲洪权[1] 魏冰冰 张正[1] 盛智勇[1] Qu Hongquan;Wei Bingbing;Zhang Zheng;Sheng Zhiyong(School of Information Science and Technology,North China University of Technology,Beijing 100144,China)

机构地区：[1]北方工业大学信息学院,北京100144

出　　处：《激光与光电子学进展》2021年第7期160-167,共8页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61571014);北京市自然科学基金(4172017)。

摘　　要：针对传统分解信号方法需要人工设定基函数,具有测不准性等问题,采用自驱动的傅里叶分解方法(FDM)处理信号,提出一种基于FDM能量熵的特征提取与识别方法。首先对振动信号进行FDM分解,得到若干个傅里叶固有带函数;然后利用自相关性原理重构信号,并提取信号FDM能量熵特征;最后将融合的特征向量送入支持向量机进行训练,并对有害振动进行识别。实验结果表明,所提方法能正确识别不同振动信号的类型,具有较高的准确率,应用于光纤预警系统中有望提高对有害振动的识别性能,促进管道保护技术的发展。Traditional signal-decomposition methods require manual setting of the basis function,which cause uncertainty and other problems.Accordingly,a self-driven Fourier decomposition method(FDM)can be used for signal processing and a feature extraction and recognition method based on FDM energy entropy is proposed in this paper.First,FDM decomposition is performed on the vibration signal to obtain several Fourier intrinsic band functions.The signal is then reconstructed based on the autocorrelation principle,and the signal FDM energy entropy feature is extracted.Finally,the fused feature vectors are sent to a support vector machine for training,and damaging vibrations are identified.Experimental results show that the proposed method can correctly identify different types of vibration signals with high accuracy.This method will enable improved recognition of damaging vibrations in optical fiber prewarning systems,thus aiding the development of pipeline protection technology.

关 键 词：光纤光学 光纤振动信号 傅里叶分解方法能量熵 基音周期 特征提取与识别 

分 类 号：TN911.6[电子电信—通信与信息系统]