Comparative analysis of temporal-spatial and time-frequency features for pattern recognition of φ-OTDR

Fanran Meng, Wenxiang Zhang, Xiaojun Liu, Fei Liu, Xian Zhou

2023Chinese Optics Letters12 citationsDOI

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.

Topics & Concepts

Optical time-domain reflectometerPattern recognition (psychology)Convolutional neural networkComputer scienceArtificial intelligenceTime domainFeature extractionNoise (video)Frequency domainTime–frequency analysisFeature (linguistics)Computer visionFiber optic sensorTelecommunicationsImage (mathematics)Optical fiberFilter (signal processing)LinguisticsGraded-index fiberPhilosophyAdvanced Fiber Optic SensorsOptical Coherence Tomography ApplicationsUltrasonics and Acoustic Wave Propagation