电子测量与仪器学报

郑来芳,张俊生,梁海坚,吕玉良.基于时频混合特征提取算法的光纤传感信号识别研究[J].电子测量与仪器学报,2020,34(9):153-159

基于时频混合特征提取算法的光纤传感信号识别研究

Time-frequency domain based hybrid feature extraction algorithm for fiber optic sensing events recognition

DOI：

英文关键词:fiber optic vibration sensing event feature extraction identification and classification

基金项目:山西省高等学校科技创新项目 ( 2019L0929 )、山西省重点研发计划 ( 201803D121069 )、教育部产学合作协同育人项目(201802022018)、太原工业学院应用型专业建设项目(2018YJ07Z)、太原工业学院教学改革研究项目(2017YJ12)资助

作者	单位
郑来芳	1.太原工业学院
张俊生	1.太原工业学院
梁海坚	1.太原工业学院
吕玉良	1.太原工业学院

Author	Institution
Zheng Laifang	1.Taiyuan Institute of Technology
Zhang Junsheng	1.Taiyuan Institute of Technology
Liang Haijian	1.Taiyuan Institute of Technology
Lv Yuliang	1.Taiyuan Institute of Technology

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中文摘要:

针对分布式光纤振动传感系统在实际应用中需对各个类别的传感事件进行快速准确的识别分类,提出了一种基于时频混合特征提取算法的识别分类方案。该方案采用具有时域特性的过零率和具有频域特性的小波包能量共同作为光纤传感事件的特征表述,而后利用基于径向基神经网络的分类器进行识别分类。经试验测试,该识别方案可以有效的从普通环境噪声中识别出光纤振动传感事件。其中,光纤振动传感事件的平均识别率为 94. 5%,识别响应时间小于 0. 3 s。

英文摘要:

To accurately and rapidly recognize and classify different kinds of sensing events in distributed fiber optic vibration sensing system, a time-frequency based hybrid feature extraction algorithm has been proposed. In the algorithm, a zero crossing rate based time domain feature vector and a wavelet packet energy based frequency domain feature vector are used as the feature description of the given sensing event. Then, the feature vectors are classified by radial basis function neural network classifier. A series of experimental results show that the vibrations can be accurately recognized from the noise with high efficiency. Specifically, the average identification rate of 94. 5% is achieved and the recognition response time can be limited in 0. 3 s.

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