A review of distributed acoustic sensing applications for railroad condition monitoring

Rahman, Md Arifur; Taheri, Hossein; Dababneh, Fadwa; Karganroudi, Sasan Sattarpanah; Arhamnamazi, Seyyedabbas

doi:10.1016/j.ymssp.2023.110983

Cited by 12 publications

(3 citation statements)

References 89 publications

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“…The results obtained by the two methods are normalized. Among them, the normalization adopts the mapping method to map the energy intensity into the interval [0,1], and the mapping method is as follows: 𝑦 = 𝑥−𝑚𝑎𝑥(𝑥) 𝑚𝑎𝑥(𝑥)−𝑚𝑖𝑛(𝑥) (5) Where 𝑥 is the calculated data, 𝑚𝑎𝑥( 𝑥) is the maximum value, 𝑚𝑖𝑛( 𝑥) is the minimum value, and 𝑦 is the result obtained by normalization of the data. The calculation results are shown in Figure 7.…”

Section: Figure 6 Time Domain Of Wrapping Part In Different Areasmentioning

confidence: 99%

“…The low loss characteristics of optical fibers give DAS systems a huge detection advantage, and the length of the sensing fiber can be easily extended to tens of kilometers, so that the detection range of DAS systems is extremely wide. The vibration detection bandwidth of DAS is up to 50 kHz, which allows it to capture subtle changes in the event under test, even if the event is far away from the fiber, which extends the detection capability of DAS technology [5]. The principle of distributed acoustic sensing fiber is mainly based on the transmission and detection of acoustic signals in the fiber.…”

Section: Introductionmentioning

confidence: 99%

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Application of Distributed Acoustic Sensing Technology in Pipeline Leakage Monitoring

Wang,

Xu,

Liu

et al. 2024

JENR

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Pipeline leak monitoring is an important industrial safety measure designed to ensure the safety of liquids or gases during transportation. Distributed acoustic sensing (DAS) technology is based on the reverse Rayleigh scattering inside the fiber to reflect the change of the measured physical quantity, and has great advantages in monitoring range, environmental adaptability, transmission loss control and system stability. In this paper, the pipeline leakage monitoring technology based on distributed acoustic sensing fiber is used to study the leakage signal of small leak aperture. In order to improve the sensitivity of leakage monitoring, the optical fiber is spiral wound on the pipe section. The identification method of pipeline leakage signal based on fast Fourier transform is proposed. By analyzing the vibration of the optical fiber in the time domain and the frequency domain, the leakage signal can be accurately monitored. Pipeline leakage tests with different leak apertures were carried out, and the leakage locations were studied by energy attenuation and cross-correlation techniques. The experimental results show that the time-domain signal fluctuates obviously and the full-band energy of the frequency-domain signal increases after pipeline leakage. The increase of leakage diameter will gradually increase the signal energy, and the leakage energy will gradually move from high frequency to low frequency. The energy attenuation positioning technique can locate the leakage within the range of a single sensing unit, and determine the leakage location through cross-correlation analysis with an error of less than 3 m.

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Section: Figure 6 Time Domain Of Wrapping Part In Different Areasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Distributed Acoustic Sensing Technology in Pipeline Leakage Monitoring

Wang,

Xu,

Liu

et al. 2024

JENR

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“…Different light scattering modes in optical fiber include Raman, Brillouin, and Rayleigh scattering (RS). 21 Figure 1 shows the characteristics of each light scattering mode in an optical fiber. In particular, DAS is an optical fiber sensing technique based on RS of light.…”

Section: Introductionmentioning

confidence: 99%

Railroad condition monitoring with distributed acoustic sensing: an investigation of deep learning methods for condition detection

Rahman,

Kim,

Dababneh

et al. 2024

J. Appl. Rem. Sens.

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Distributed acoustic sensing (DAS) using fiber optic cables over an extensive length of railroads is a well-suited technique for condition monitoring (CM) of railroads. Regardless of the type of indication in railroad CM, the original large and noisy dataset from the DAS system is a major challenge in DAS data analysis. Different data analysis strategies, such as conventional peak finding or neural networks, can be considered for DAS data analysis depending on the purpose of the study and characteristics of the railroad. We aim to investigate the robustness of deep learning (DL) models based on long-shot-term memory (LSTM) and gated recurrent unit (GRU) approaches. The average trend of the recorded technical data management streaming signals was used to extract the train presence or absence conditions along the railroad. This investigation showed that DL approaches could be efficient for DAS signal processing and CM in railroad infrastructures and can be expanded in the future for other CM purposes such as flaw detection. Meanwhile, for train position monitoring, the proposed model based on the GRU architecture indicated a 94% detection rate compared with 93% by the LSTM model. In all, the proposed models show promising potential for efficiently detecting railroad conditions, such as anomalies and flaws that require further investigation.

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In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

Rahman,

Jamal,

Cruz

et al. 2024

Int J Adv Manuf Technol

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A review of distributed acoustic sensing applications for railroad condition monitoring

Cited by 12 publications

References 89 publications

Application of Distributed Acoustic Sensing Technology in Pipeline Leakage Monitoring

Application of Distributed Acoustic Sensing Technology in Pipeline Leakage Monitoring

Railroad condition monitoring with distributed acoustic sensing: an investigation of deep learning methods for condition detection

In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

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