A Novel Method for Railway Crossing Monitoring Based on Ambient Vibration Caused by Train-Track Interaction

Shen, C. S.; Li, Zili; Dollevoet, Rolf

doi:10.1007/978-3-030-38077-9_16

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Track condition monitoring applications are developed on the basis of acceleration sensors [ 6 ] or strain gauges [ 7 ], mainly for the condition monitoring of a crossing [ 8 , 9 , 10 ]. Bridge monitoring solutions have also been widely discussed in recent publications.…”

Section: Energy Harvesting Technologies For Trackside Applicationsmentioning

confidence: 99%

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Hadaš

Rubes

Ksica

et al. 2022

Sensors

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This paper deals with a development and lab testing of energy harvesting technology for autonomous sensing in railway applications. Moving trains are subjected to high levels of vibrations and rail deformations that could be converted via energy harvesting into useful electricity. Modern maintenance solutions of a rail trackside typically consist of a large number of integrated sensing systems, which greatly benefit from autonomous source of energy. Although the amount of energy provided by conventional energy harvesting devices is usually only around several milliwatts, it is sufficient as a source of electrical power for low power sensing devices. The main aim of this paper is to design and test a kinetic electromagnetic energy harvesting system that could use energy from a passing train to deliver sufficient electrical power for sensing nodes. Measured mechanical vibrations of regional and express trains were used in laboratory testing of the developed energy harvesting device with an integrated resistive load and wireless transmission system, and based on these tests the proposed technology shows a high potential for railway applications.

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Section: Energy Harvesting Technologies For Trackside Applicationsmentioning

confidence: 99%

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Hadaš

Rubes

Ksica

et al. 2022

Sensors

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show abstract

“…In the former category, sensors are usually mounted on rails (Yue et al., 2016), sleepers (Le Pen et al., 2016), ballast (Liu et al., 2021), or concrete slabs (Zhang et al., 2021) to measure their dynamic response under train passage and to support the identification of track properties. However, these techniques are cost‐prohibitive for large‐scale infrastructure monitoring, so they are usually applied at hot spots, such as joints (Yang et al., 2018) and crossings (Boogaard et al., 2018; Shen et al., 2019). In the category of train‐borne measurements, sensors are mounted on trains, such as on their axle boxes or bogie frames, to detect anomalies in railway tracks (Bocciolone et al., 2007; Li et al., 2022; Salvador et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Railway sleeper vibration measurement by train‐borne laser Doppler vibrometer and its speed‐dependent characteristics

Zeng,

Núñez,

2024

Computer aided Civil Eng

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A train‐borne laser Doppler vibrometer (LDV) directly measures the dynamic response of railway track components from a moving train, which has the potential to complement existing train‐borne technologies for railway track monitoring. This paper proposes a holistic methodology to characterize train‐borne LDV measurements by combining computer‐aided approaches and real‐life measurements. The focus is on the speed‐dependent characteristics because the train speed affects the intensity of railway sleeper vibrations and the intensity of speckle noise, which further affects the quality and usability of the measured signals. First, numerical models are established and validated to simulate sleeper vibrations and speckle noise separately. Then, a vibration–noise separation method is proposed to effectively extract speckle noise and structural vibrations from LDV signals measured at different speeds. The parameters of the separation method are tuned using simulation signals. The method is then validated using laboratory measurements in a vehicle‐track test rig and applied to field measurements on a railway track in Rotterdam, the Netherlands. Further, the speed‐dependent characteristics of train‐borne LDV measurement are determined by analyzing the competition between sleeper vibrations and speckle noise at different speeds. Simulation and measurement results show that an optimal speed range yields the highest signal‐to‐noise ratio, which varies for different track structures, measurement configurations, and operational conditions. The findings demonstrate the potential of train‐borne LDV for large‐scale rail infrastructure monitoring.

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“…A study of railway sleepers under train passage is used to showcase the proposed method. EMA, especially hammer tests, has been widely applied in the modal analysis and SHM of railway tracks [35][36][37], whereas the application of OMA is rare [38]. Furthermore, the characteristics of the train-induced load on a sleeper vary considerably as the train approaches, passes, and leaves, which further afect the stifness and damping of track components [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Method for Operational Modal Analysis in Time-Frequency Representation and Its Applications to Railway Sleepers

Zeng

Shen

Núñez

et al. 2023

Structural Control and Health Monitoring

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Operational modal analysis (OMA) enables the identification of modal characteristics under operational loads and conditions. Traditional frequency-domain methods cannot directly capture modal changes over time, while existing time-frequency representations are not sufficiently interpretable due to spurious modes and implicit parameter design. This paper develops a new OMA method in time-frequency representation based on frequency-domain decomposition (FDD). Short-time FDD and a convolution-based strategy are proposed to obtain singular values and local mode shape similarity, respectively, which are further fused into mode indicators by a fuzzy-based strategy mimicking the modal assurance criterion. The method provides not only a global view of the modal characteristics over time and frequency but also estimates of the modal parameters. It is applicable to strongly nonstationary responses under time-varying loads and conditions. All the parameters explicitly affect the time-frequency representation, and the interpretability is enhanced by including physical information from the user’s prior knowledge in selecting parameters and peak bands. The proposed method is validated based on a study of railway sleepers under train passage. The rigid-body motions and bending modes are identified at frequencies up to 6,500 Hz in laboratory tests and 4,500 Hz in field tests at speeds up to 200 km/h. The identified natural frequencies and mode shapes agree with experimental modal analysis (EMA). The proposed method outperforms EMA in terms of broad frequency range and low measurement cost and can be potentially applied to structural health monitoring under operational conditions.

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A Novel Method for Railway Crossing Monitoring Based on Ambient Vibration Caused by Train-Track Interaction

Cited by 6 publications

References 10 publications

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

Railway sleeper vibration measurement by train‐borne laser Doppler vibrometer and its speed‐dependent characteristics

An Interpretable Method for Operational Modal Analysis in Time-Frequency Representation and Its Applications to Railway Sleepers

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