Detection and identification of external intrusion signals from 33 km optical fiber sensing system based on deep learning

Bai, Yu; Xing, Jichuan; Xie, Fei; Liu, Sujie; Li, Jinxin

doi:10.1016/j.yofte.2019.102060

Cited by 44 publications

(20 citation statements)

References 22 publications

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“…Although the conventional Φ-OTDR system can locate external disturbances, it is insufficient for distinguishing different types of intrusion events. In order to solve this problem, pattern recognition algorithms have been extensively studied for Φ-OTDR signal post-processing in recent years [102][103][104][105][106][107][108][109][110][111][112] . Pattern recognition algorithms can automatically classify the detected vibration signals into intrusion of interest, and undesired environmental noise, according to their signal features, thus dramatically increasing the alarm accuracy and reducing false alarm rate of the system.…”

Section: Event Discriminationmentioning

confidence: 99%

“…With appropriate optical configurations, Φ-OTDR is capable of measuring vibration 23 , strain or temperature distribution 36,37,175 at high spatial resolution (~m) over long distance (~km). Such ability makes Φ-OTDR a promising tool in various scenarios, including geological exploration , perimeter monitoring [102][103][104][105][106][107][108][109][110][111][112] , traffic sensing 73,[113][114][115][116][117][118] , partial discharge monitoring 78,[119][120][121] , and other novel applications 94,95,[176][177][178][179][180][181][182][183] . This section reviews the recent progress of advancing Φ-OTDR applications in separate fields.…”

Section: Applications Of φ-Otdrmentioning

confidence: 99%

“…Because Φ-OTDR offers meter-level spatial resolution over the range of tens of kilometers, it has been utilized in geological exploration for borehole and surface seismic wave detection , providing a new solution to study microseism for geologists. By employing pattern recognition algorithms in the signal post-processing stage, Φ-OTDR is also a promising tool for perimeter monitoring with the capability of accurate locating and recognizing intrusions [102][103][104][105][106][107][108][109][110][111][112] . Besides, Φ-OTDR has been applied for vehicle and train tracking for its broadband response and long coverage 73,[113][114][115][116][117][118] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advances in phase-sensitive optical time-domain reflectometry

Liu¹,

Yu²,

Hong³

et al. 2022

OEA

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Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted numerous attention due to its superior performance in detecting the weak perturbations along the fiber. Relying on the ultra-sensitivity of light phase to the tiny deformation of optical fiber, Φ-OTDR has been treated as a powerful technique with a wide range of applications. It is fundamental to extract the phase of scattering light wave accurately and the methods include coherent detection, I/Q demodulation, 3 by 3 coupler, dual probe pulses, and so on. Meanwhile, researchers have also made great efforts to improve the performance of Φ -OTDR. The frequency response range, the measurement accuracy, the sensing distance, the spatial resolution, and the accuracy of event discrimination, all have been enhanced by various techniques. Furthermore, lots of researches on the applications in various kinds of fields have been carried out, where certain modifications and techniques have been developed. Therefore, Φ-OTDR remains as a booming technique in both researches and applications.

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Section: Event Discriminationmentioning

confidence: 99%

Section: Applications Of φ-Otdrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advances in phase-sensitive optical time-domain reflectometry

Liu¹,

Yu²,

Hong³

et al. 2022

OEA

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

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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.

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“…The safety monitoring and evaluation of civil infrastructures increasingly become an effective method for research on damage evolution behaviour and an important operational safety guarantee technology. However, although structural safety monitoring has been applied in some practical projects, the theoretical research and application of structural safety monitoring is still in its infancy [25]. Most of the monitoring systems installed in practical projects use traditional sensors.…”

Section: Introductionmentioning

confidence: 99%

AI-Based Self-Learning System in Distributed Structural Health Monitoring and Control

Yan

Lin

et al. 2021

Neural Process Lett

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Artificial intelligence is predicted to play a big part in self-learning, industrial automation that will negotiate the bandwidth of structural health and control systems. The industrial structural health and control system based on discrete sensors possesses insufficient spatial coverage of sensing information, while the distributed condition monitoring has been mainly studied at the sensor level, relatively few studies have been conducted at the artificial intelligence level. This paper presents an innovative method for distributed structural health and control systems based on artificial intelligence. The structural condition was divided into regional and local features, the feature extraction and characterization are performed separately. Structural abnormality recognition and risk factor calculation method were proposed by considering the response values and the distribution patterns of both the regional and the local structural behaviours. The test results show that the method can effectively identify the full-scale and local damage of the structure, respectively. Subsequently, structural safety assessment method for long-span structures at kilometres level in view of fully length strain distributions measured by distributed fiber optic sensors were developed. A series of load tests on the long-span structure were carried out. Finite element (FE) model was developed using finite element code, ABAQUS, and an extensive parametric study was conduct to explore the effect of load cases on the structural responses. The differences in the structural response results among load test, structural safety assessment and FE simulation were investigated. It is shown that AI-based self-learning system could offer suitable speed in deployment, reliability in solution and flexibility to adjust in distributed structural health monitoring and control.

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Detection and identification of external intrusion signals from 33 km optical fiber sensing system based on deep learning

Cited by 44 publications

References 22 publications

Advances in phase-sensitive optical time-domain reflectometry

Advances in phase-sensitive optical time-domain reflectometry

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

AI-Based Self-Learning System in Distributed Structural Health Monitoring and Control

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Detection and identification of external intrusion signals from 33 km optical fiber sensing system based on deep learning

Cited by 44 publications

References 22 publications

Advances in phase-sensitive optical time-domain reflectometry

Advances in phase-sensitive optical time-domain reflectometry

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

AI-Based Self-Learning System in Distributed Structural Health Monitoring and Control

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Detection and identification of external intrusion signals from 33 km optical fiber sensing system based on deep learning