Abstract

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 in φ-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.

© 2023 Chinese Laser Press

Highly discriminative and adaptive feature extraction method based on NMF–MFCC for event recognition of Φ-OTDR

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Machine learning methods for identification and classification of events in ϕ-OTDR systems: a review

Deus F. Kandamali, Xiaomin Cao, Manling Tian, Zhiyan Jin, Hui Dong, and Kuanglu Yu
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