Study on structural health monitoring of vertical vibration of ballasted track in high-speed railway

Liu, Ganzhong; Wang, Ping; Liu, Jianxing; Xiao, Jieling; Chen, Rong; Wei, Xian‐Kui

doi:10.1007/s13349-020-00460-x

Cited by 16 publications

(10 citation statements)

References 40 publications

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“…To eliminate relative motion between the development board and ballast particle, highstrength AB adhesive with a tensile shear strength of 12.4 MPa at room temperature was used in the packaging. The constructed device could sense the motion state of a ballast particle [28], as shown in Fig. 2.…”

Section: Test Objectmentioning

confidence: 99%

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Study on vertical vibration and transmission characteristics of railway ballast using impact hammer test

Liu

Cong

Wang

et al. 2022

Construction and Building Materials

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Section: Test Objectmentioning

confidence: 99%

“…The direction vector V of the ballast sensor can be rotated to coincide with the z-axis of the coordinate system through two coordinate rotations. The correction matrix is [28…”

Section: Vertical Axis Calibration Of Ballast Sensormentioning

confidence: 99%

Study on vertical vibration and transmission characteristics of railway ballast using impact hammer test

Liu

Cong

Wang

et al. 2022

Construction and Building Materials

Self Cite

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“…The operating mileage of China’s high-speed railway has exceeded 42,000 km [ 1 ], and it has changed to the long-term operation and maintenance stage from the large-scale construction stage [ 2 ]. It is more and more critical to manage and maintain the large-scale high-speed track lines [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Cheng

Yao

Yang

et al. 2022

Sensors

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With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.

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“…The method enables fast predictions for large datasets and high accuracy of the identification. Another vibration-based method for ballasted track monitoring is presented in the study [ 17 ]. The information source was wireless inertial sensors embedded into the ballast particles.…”

Section: Introductionmentioning

confidence: 99%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

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The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

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Study on structural health monitoring of vertical vibration of ballasted track in high-speed railway

Cited by 16 publications

References 40 publications

Study on vertical vibration and transmission characteristics of railway ballast using impact hammer test

Study on vertical vibration and transmission characteristics of railway ballast using impact hammer test

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

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