Analyzing Distributed Vibrating Sensing Technologies in Optical Meshes

Rahman, Saifur; Ali, Farman; Muhammad, Fazal; Irfan, Muhammad; Głowacz, Adam; Akond, Mohammed Shahed; Armghan, Ammar; Mursal, Salim Nasar Faraj; Ali, Amjad; Alkahtani, Fahad

doi:10.3390/mi13010085

Cited by 1 publication

(1 citation statement)

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“…In [ 18 , 19 , 20 , 21 ], similar studies have been carried out to improve the quality of the signal, extract features, and develop algorithms to classify acoustic effects recorded by a phase-sensitive reflectometer. In the considered works, studies on simplified data augmentation, taking into account the preservation of the physical meaning of the signals, were not carried out.…”

Section: Introductionmentioning

confidence: 99%

Classification of Acoustic Influences Registered with Phase-Sensitive OTDR Using Pattern Recognition Methods

Barantsov

Pnev

Koshelev

et al. 2023

Sensors

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This article is devoted to the development of a classification method based on an artificial neural network architecture to solve the problem of recognizing the sources of acoustic influences recorded by a phase-sensitive OTDR. At the initial stage of signal processing, we propose the use of a band-pass filter to collect data sets with an increased signal-to-noise ratio. When solving the classification problem, we study three widely used convolutional neural network architectures: AlexNet, ResNet50, and DenseNet169. As a result of computational experiments, it is shown that the AlexNet and DenseNet169 architectures can obtain accuracies above 90%. In addition, we propose a novel CNN architecture based on AlexNet, which obtains the best results; in particular, its accuracy is above 98%. The advantages of the proposed model include low power consumption (400 mW) and high speed (0.032 s per net evaluation). In further studies, in order to increase the accuracy, reliability, and data invariance, the use of new algorithms for the filtering and extraction of acoustic signals recorded by a phase-sensitive reflectometer will be considered.

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