摘要
The phase-sensitive time-domain reflectometer [φ-OTDR] has been popularly used for events detection over a long period of time.In this study,the events classification methods based on convolutional neural networks [CNNs] with different features,i.e.,the temporal-spatial features and time-frequency features,are compared and analyzed comprehensively inφ-OTDR.The developed CNNs aim at distinguishing three typical events:wind blowing,knocking,and background noise.The classification accuracy based on temporal-spatial images is higher than that based on time-frequency images[99.49% versus 98.23%).The work here sets a meaningful reference for feature extraction and application in the pattern recognition of φ-OTDR.
作者
孟凡然
张文祥
刘晓军
刘飞
周娴
Fanran Meng;Wenxiang Zhang;Xiaojun Liu;Fei Liu;Xian Zhou(School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China)
基金
supported by the Fundamental Research Funds for the Central Universities(No.FRF-TP-20-067A1Z)。
