Abstract
This paper proposes a multi-radial-distance event classification method based on deep learning. To the best of our knowledge, this is the first time that the $\Phi $ -OTDR can tell how far the target event from the sensing fiber is through deep learning approach. The temporal-spatial data matrix collected by the system is filtered by three different band-pass filters to form RGB images as the input of the Inception_V3 network trained by ImageNet dataset. The passband of three band-pass filters is selected by searching the maximum Euclidean distance in the frequency domain. Three kinds of filters with different frequency bands enhance the effective features of data samples in advance. The simulated annealing (SA) algorithm is applied to search the maximum Euclidean distance. Field experiment includes five kinds of events with four different radial distances, where there are 17 subclasses in total, has been carried out. The classification results show that the classification accuracy reaches 86% and the method can tell both the event type and radial distance.
Highlights
Distributed optical fiber sensing is a technology that collects signals through multiple sensing units in a single sensing optical fiber
To the best of our knowledge, this is the first time that the -OTDR can tell how far the target event from the sensing fiber is through deep learning approach
This paper has proposed a multi-radial-distance event recognition method for -OTDR distributed optical fiber sensing system based on deep learning
Summary
Distributed optical fiber sensing is a technology that collects signals through multiple sensing units in a single sensing optical fiber. This paper proposes a multi-radial-distance event recognition method based on deep learning and considering the frequency transmission differences for -OTDR distributed optical fiber sensing system. The first method uses a single-channel grayscale picture (narrow-bandwidth data) generated from the original data matrix by a narrow-bandwidth bandpass filter with bandwidth from 190Hz to 210Hz, where there are the biggest differences in frequency domain as shown in Fig.. The proposed three-channel method shows the best performance (86.82% classification accuracy) as the relative change between the three frequency bands gives more difference and is not obviously affected by the backscattering light intensity fluctuation, which is common in -OTDR system due to slow change of system parameters.
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