A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

Wu, Huijuan; Yang, Mingru; Yang, Siqi; Lu, Hao; Wang, Chaoqun; Rao, Yunjiang

doi:10.1109/access.2020.3004207

Cited by 57 publications

(11 citation statements)

References 32 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extracting more and more accurate information to understand the detected vibration or acoustic targets better has always been an important goal in signal recognition for DAS. In [92], a novel identification method was then proposed by treating the spatial-and temporal-information in a different way, which is denoted as the 1DCNNs-bidirectional long short term memory (1DCNNs-BiLSTM) model as shown in Fig. 37.…”

Section: Applications Of Artificial Intelligence (Ai) To ф-Otdr/dasmentioning

confidence: 99%

Recent Advances in Phase-Sensitive Optical Time Domain Reflectometry (Ф-OTDR)

Rao

Wang

et al. 2021

Photonic Sens

Self Cite

View full text Add to dashboard Cite

Phase-sensitive optical time domain reflectometry (Ф-OTDR) is an effective way to detect vibrations and acoustic waves with high sensitivity, by interrogating coherent Rayleigh backscattering light in sensing fiber. In particular, fiber-optic distributed acoustic sensing (DAS) based on the Ф-OTDR with phase demodulation has been extensively studied and widely used in intrusion detection, borehole seismic acquisition, structure health monitoring, etc., in recent years, with superior advantages such as long sensing range, fast response speed, wide sensing bandwidth, low operation cost and long service lifetime. Significant advances in research and development (R&D) of Ф-OTDR have been made since 2014. In this review, we present a historical review of Ф-OTDR and then summarize the recent progress of Ф-OTDR in the Fiber Optics Research Center (FORC) at University of Electronic Science and Technology of China (UESTC), which is the first group to carry out R&D of Ф-OTDR and invent ultra-sensitive DAS (uDAS) seismometer in China which is elected as one of the ten most significant technology advances of PetroChina in 2019. It can be seen that the Ф-OTDR/DAS technology is currently under its rapid development stage and would reach its climax in the next 5 years.

show abstract

Section: Applications Of Artificial Intelligence (Ai) To ф-Otdr/dasmentioning

confidence: 99%

Recent Advances in Phase-Sensitive Optical Time Domain Reflectometry (Ф-OTDR)

Rao

Wang

et al. 2021

Photonic Sens

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many research works that combine DAS + PRS suffer from issues related to pattern classification design and experimental evaluation setups: real classification and results are not presented [45][46][47][48][49][50]; lack of details on both the system description and experimental conditions [25,[51][52][53][54][55][56][57][58][59]; data are not obtained in realistic field environments [15,25,49,[51][52][53]58,[60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77]; the lack of testing signals/classes to recognize [15,49,54,63,65,[69][70]…”

Section: Motivation and Organization Of This Papermentioning

confidence: 99%

A Multi-Position Approach in a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection

et al. 2021

View full text Add to dashboard Cite

We present a new pipeline integrity surveillance system for long gas pipeline threat detection and classification. The system is based on distributed acoustic sensing with phase-sensitive optical time domain reflectometry (ϕ-OTDR) and pattern recognition for event classification. The proposal incorporates a multi-position approach in a Gaussian Mixture Model (GMM)-based pattern classification system which operates in a real-field scenario with a thorough experimental procedure. The objective is exploiting the availability of vibration-related data at positions nearby the one actually producing the main disturbance to improve the robustness of the trained models. The system integrates two classification tasks: (1) machine + activity identification, which identifies the machine that is working over the pipeline along with the activity being carried out, and (2) threat detection, which aims to detect suspicious threats for the pipeline integrity (independently of the activity being carried out). For the machine + activity identification mode, the multi-position approach for model training obtains better performance than the previously presented single-position approach for activities that show consistent behavior and high energy (between 6% and 11% absolute) with an overall increase of 3% absolute in the classification accuracy. For the threat detection mode, the proposed approach gets an 8% absolute reduction in the false alarm rate with an overall increase of 4.5% absolute in the classification accuracy.

show abstract

“…Since the processed signals are a temporary process, solutions have been proposed using various types of RNN [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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

Barantsov

Pnev

Koshelev

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

Cited by 57 publications

References 32 publications

Recent Advances in Phase-Sensitive Optical Time Domain Reflectometry (Ф-OTDR)

Recent Advances in Phase-Sensitive Optical Time Domain Reflectometry (Ф-OTDR)

A Multi-Position Approach in a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection

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

Contact Info

Product

Resources

About