Artificial Intelligent Pattern Recognition for Optical Fiber Distributed Acoustic Sensing Systems Based on Phase-OTDR

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.

Section: Introductionmentioning

confidence: 99%

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

Barantsov

Pnev

Koshelev

et al. 2023

“…As the vibration is located through the intensity information, distributed vibration sensing (DVS) is realized [ 18 , 19 , 20 , 21 , 22 , 23 ]. Later developments showed that the demodulation of the phase will help us to obtain the waveform of the vibration with high definition, which is the basis of distributed acoustic sensing (DAS) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. DAS has been rapidly commercialized and widely considered in many industrial applications, including pipeline monitoring [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], railway and highway transportation [ 23 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ], structural inspection [ 47 , 48 , 49 , 50 ], perimeter security [ 51 , 52 , 53 , 54 , 55 ], geophysics [ 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 <...…”

Section: Introductionmentioning

confidence: 99%

Hybrid Distributed Optical Fiber Sensor for the Multi-Parameter Measurements

Zhou

Wang

Yang

et al. 2023

Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications.

“…The 2 of 17 accuracy is 86%. In [10], Hongqiao Wen et al use a CNN to perform a spectrogram analysis. The problem to be solved is the classification of five types of events.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Classifier for Recognizing Acoustic Impacts Recorded with a phi-OTDR

Barantsov,

Pnev,

Koshelev

et al. 2023

The purpose of this work is to increase the security of the perimeter of an area from unauthorized intrusions by creating an improved algorithm for classifying acoustic impacts recorded with a sensor system based on a phase-sensitive optical time reflectometer (phi-OTDR). The algorithm includes machine learning, so a dataset consisting of two classes was assembled. The dataset consists of two classes. The first class is the data of the steps, and the second class is other non-stepping influences (engine noise, a passing car, a passing cyclist, etc.). As an intrusion signal, a human walking signal is analyzed and recorded in frames of 5 s, which passed the threshold condition. Since, in most cases, the intruder moves on foot to overcome the perimeter, the analysis of the acoustic effects generated during the step will increase the efficiency of the perimeter detection tools. When walking quietly, step signals can be quite weak, and background signals can contain high energy and visually resemble the signals you are looking for. Therefore, an algorithm was created that processes space–time diagrams developed in real time, which are grayscale images. At the same time, during the processing of one image, two more images are calculated, which are the result of processing the denoised autoencoder and the created mathematical model of the adaptive correlation. Then, the three obtained images are fed to the input of the created three-channel neural network classifier, which includes convolutional layers for the automatic extraction of spatial features. The probability of correctly detecting steps is 98.3% and that of background actions is 97.93%.