Fiber optic sensing for monitoring structure and health of railway infrastructures

Chafiq, Yassine; Tatin, Maxime.; Postaspana, Olivier.; Paris, Jean-Baptiste; Maraval, Damien; Lamour, Vincent

doi:10.1364/ofs.2018.the97

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…In [7], [8], we showed that most of the DAS+PRS presented to that date had significant issues with respect to the pattern classification design and experimental evaluation procedures. Since then, new works have been presented [9]- [26], though they also suffer from similar issues, as there is a lack of rigorous and realistic experimental procedures: no real classification nor results are presented [10], [14], [15], [21]; not enough details on the system description or experimental procedure are provided [11]- [13], [17], [18], [26]; the data were not acquired in a realistic field environment [9], [11]- [13], [16], [17], [19], [20], [22]- [25]; and the lack of testing signals [18], [20], [23].…”

Section: Introductionmentioning

confidence: 99%

A Contextual GMM-HMM Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection

Tejedor

Macías-Guarasa

Martins³

et al. 2019

J. Lightwave Technol.

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This paper presents a novel pipeline integrity surveillance system aimed to the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry (φ-OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level in a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM)-based pattern classification system and applies a system combination strategy for acoustic trace decision. System combination relies on majority voting of the decisions given by the individual contextual information sources and the number of states used for HMM modelling. The system runs in two different modes:(1) machine+activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed to detect threats no matter what the real activity being conducted is. In comparison with the previous systems based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information and the GMM-HMM approach improves the results for both machine+activity identification (7.6% of relative improvement with respect to the best published result in the literature on this task) and threat detection (26.6% of relative improvement in the false alarm rate with 2.1% relative reduction in the threat detection rate).

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Section: Introductionmentioning

confidence: 99%

A Contextual GMM-HMM Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection

Tejedor

Macías-Guarasa

Martins³

et al. 2019

J. Lightwave Technol.

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“…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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“…If the DAS system connects to an optical fiber cable, each point along the fiber becomes a sensing unit, achieving continuous detection along the length of the road. The DAS system can continuously monitor fluctuation signals caused by external vibrations, and these are called acoustic events [15][16][17]. DAS data can be analyzed, which helps to locate acoustic events along optical fibers and classify them (such as cars, trucks, etc.).…”

Section: Introductionmentioning

confidence: 99%

Vehicle Operation Status Monitoring Based on Distributed Acoustic Sensor

Chen,

Ding,

Liu

et al. 2023

Sensors

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To develop implementation research on distributed optical fiber sensing technology, field tests were conducted on municipal roads and railways using a distributed acoustic sensor (DAS). Data were collected by the DAS during a field test for a long time period (more than 20 min), and we conducted short-term (<10 s) and long-term (≥10 s) analyses on these data separately. In the short-term data analysis, the vehicle type, vehicle length, and working status of the vehicle engine or the compressor were identified. In the long-term data analysis, the traffic flow was monitored, and the running distance, acceleration, speed, and braking distance of the vehicle were obtained. The characteristics of the vehicle operation data obtained in these field tests are important in developing the data processing method of DASs, which will help to promote the implementation of DASs.

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Fiber optic sensing for monitoring structure and health of railway infrastructures

Cited by 4 publications

References 2 publications

A Contextual GMM-HMM Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection

A Contextual GMM-HMM Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection

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

Vehicle Operation Status Monitoring Based on Distributed Acoustic Sensor

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