WSN-Based Vibration Characteristic Research for Various Railway Track Structures for Pattern Classification

Yue, Guodong; Xu, Zheng; Wang, Liding; Liu, Chong; Ren, Tongqun

doi:10.1142/s0218001416500208

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…To record the vertical vibration time histories of the acceleration nodes, the acquisition time was set as 4 s with a high acquisition frequency of 5 kHz. The setup is reported in Yue et al 22 (Figure 3).…”

Section: Wsn-based Track Vibration Experimentsmentioning

confidence: 99%

Vibration analysis for slab track at different train speeds using Bayes wavelet denoising

Yue

Wang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part F:

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With increasing train speed, it is necessary to study the relation between slab track vibration and travelling speed. In this paper, a wireless sensor network system is deployed on a typical ballastless slab track in China, which uses acceleration nodes to collect vibration information when the train is passing by at different constant speeds. Then, after comparing the denoising effects of a hard threshold method, a soft threshold method and a Bayes wavelet method, the Bayes wavelet denoising method is used to remove the noise, while the spatial variability characteristics of the signal are preserved. Finally, the wavelet energy spectrum is adopted to obtain the duration and energy of the non-stationary vibration data. The time-frequency function curve is obtained to further analyse the physical behaviour of the vehicletrack system. A hammering experiment is conducted to show the importance of the results. This work facilitates a better understanding of the track vibration characteristics for monitoring the status of the track.

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Section: Wsn-based Track Vibration Experimentsmentioning

confidence: 99%

Vibration analysis for slab track at different train speeds using Bayes wavelet denoising

Yue

Wang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…Existing vibration‐based techniques for monitoring railway track dynamic properties can be divided into trackside and train‐borne vibration measurements. 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).…”

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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“…ese make rail prone to failure and cause serious accidents. Because the rail base is at the lower part of the rail and is affected by the sleepers, it has been and still a crucial research issue to monitor the safe status of rail base [4]. e rail is a highly directional structure and is extremely suitable for guided wave inspection.…”

Section: Introductionmentioning

confidence: 99%

Guided Wave Propagation for Monitoring the Rail Base

Yue

Cui

Zhang³

et al. 2020

Mathematical Problems in Engineering

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In order to monitor the rail base, the dispersion characteristics and propagation properties of the guided wave are studied. Firstly, two modes named as Modes V1 and V2 are selected by the semianalytical finite element method (SAFE). The region at the bottom edge can be monitored by Mode V1, while the junction of the base edge and the flange can be detected by Mode V2. Then, the characteristics in the propagation process are analyzed using the finite element method (FEM). The two modes can be separated about 0.6 ms after they are excited. Thirdly, a wave attenuation algorithm based on mean is proposed to quantify the wave attenuation. Both waves can have weak attenuation and be detected within 5 m. Finally, a mode-identified experiment is performed to validate the aforementioned analysis. And a defect detection experiment is performed to demonstrate the excellent monitoring characteristics using Mode V2. These results can be used to monitor the rail base in practice engineering.

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WSN-Based Vibration Characteristic Research for Various Railway Track Structures for Pattern Classification

Cited by 7 publications

References 23 publications

Vibration analysis for slab track at different train speeds using Bayes wavelet denoising

Vibration analysis for slab track at different train speeds using Bayes wavelet denoising

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

Guided Wave Propagation for Monitoring the Rail Base

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