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. 2023 May 22;23(10):4978.
doi: 10.3390/s23104978.

Fiber-Optic Telecommunication Network Wells Monitoring by Phase-Sensitive Optical Time-Domain Reflectometer with Disturbance Recognition

Andrey A Zhirnov  1 , German Y Chesnokov  2 , Konstantin V Stepanov  1 , Tatyana V Gritsenko  1 , Roman I Khan  1 , Kirill I Koshelev  1 , Anton O Chernutsky  1 , Cesare Svelto  3 , Alexey B Pnev  1 , Olga V Valba  2   4
Affiliations

Fiber-Optic Telecommunication Network Wells Monitoring by Phase-Sensitive Optical Time-Domain Reflectometer with Disturbance Recognition

Andrey A Zhirnov et al. Sensors (Basel). .

Abstract

The paper presents the application of a phase-sensitive optical time-domain reflectometer (phi-OTDR) in the field of urban infrastructure monitoring. In particular, the branched structure of the urban network of telecommunication wells. The encountered tasks and difficulties are described. The possibilities of usage are substantiated, and the numerical values of the event quality classification algorithms applied to experimental data are calculated using machine learning methods. Among the considered methods, the best results were shown by convolutional neural networks, with a probability of correct classification as high as 98.55%.

Keywords: acoustic monitoring; distributed fiber optic sensor; fiber optic sensor; machine learning; phi-OTDR; telecommunication well.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Scheme of a sensor based on phi-OTDR.
Figure 2
Figure 2
Examples of actions with fiber sensors. (a) Different environmental noises and signals on the phi-OTDR waterfall plot; (b) signal evolution in time during intrusion into a manhole; (c) common scheme of intrusion events.
Figure 2
Figure 2
Examples of actions with fiber sensors. (a) Different environmental noises and signals on the phi-OTDR waterfall plot; (b) signal evolution in time during intrusion into a manhole; (c) common scheme of intrusion events.
Figure 3
Figure 3
Example of a signal spectrogram. It can be seen that the effect of the disturbance is manifested in the entire spectral range recorded by the device. The main frequencies are in the range from 15 to 70 Hz.
Figure 4
Figure 4
Examples of signals in different wells: green line according to wells with intrusion, environmental, and system noises; blue and orange curves according to wells with no intrusion, just environmental and system noises: (a) intensity on the photodiode without any processing; (b) standard deviation of intensity on the photodiode in a sliding 5-s window.
Figure 4
Figure 4
Examples of signals in different wells: green line according to wells with intrusion, environmental, and system noises; blue and orange curves according to wells with no intrusion, just environmental and system noises: (a) intensity on the photodiode without any processing; (b) standard deviation of intensity on the photodiode in a sliding 5-s window.
Figure 5
Figure 5
Histogram of the number of event types.
Figure 6
Figure 6
Visualization representation of different feature spaces: (a) SD, (b) FBE, (c) WPE, and (d) values of activations on the penultimate layer.
See this image and copyright information in PMC

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