Identification method for subgrade settlement of ballastless track based on vehicle vibration signals and machine learning

Ren, Juanjuan; Liu, Wei; Du, Yong; Zheng, Jianlong; Wei, Hui; Zhang, Kai-yao; Ye, Wen-long

doi:10.1016/j.conbuildmat.2023.130573

Cited by 25 publications

(4 citation statements)

References 29 publications

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“…To better characterize the feature of the track irregularity and vehicle vibration data, 12 time-domain indexes are selected: 39 1) Absolute Peak Value ( X p ): the maximum absolute value of the sample points in the signal.2) Peak-to-Peak Value ( X pp ): the difference between the maximum and minimum values in the signal.3) Absolute Mean Value ( X ave ): the average of the absolute values of all sample points in the signal.4) Variance (σ2): the average of the squares of the differences between the signal samples and their mean value.5) Standard Deviation (σ): the square root of the variance.6) Root Mean Square ( X RMS ): the square root of the mean of the squares of the signal samples.where N refers to the length of X .…”

Section: Track Inspection Data Analysis Methodsmentioning

confidence: 99%

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Feature analysis of precipitation-induced subgrade defects on a high-speed rail ballasted track using multiple track inspection data: A case study

Xiao,

Bai,

Song

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part F:

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Heavy rainfall has posed a great challenge to the service performance of high-speed rail (HSR) substructure, resulting in a reduction in the ride quality and safety of high-speed trains. To carry out proper repair work for the substructure, it is imperative to realize efficient identification of precipitation-induced subgrade defects. To this end, this paper aims to extract the features of typical precipitation-induced subgrade defects from the multiple track inspection data to provide a basis for defect identification. Firstly, the geotechnical site investigation including Ground Penetrating Radar (GPR) detection, moisture content test, and dynamic cone penetration (DCP) test of a typical defective spot is performed to determine the condition of the subgrade after heavy rainfall; then, the analysis methods of track inspection data are introduced; finally, the track geometry data and carbody acceleration data of four typical defective sections are analyzed, and the time-domain, frequency-domain and discrete wavelet transform (DWT)-based features which are highly correlated with the precipitation-induced subgrade defects are extracted. The results show that the feature indexes extracted from track surface irregularity and carbody vertical acceleration increase significantly after heavy rainfall; the long wavelength components (8 m and above) of both track irregularity and carbody vibration are more sensitive to the subgrade defects, which is reflected by the sharp increase of the DWT-based features at some levels corresponding to long wavelength ranges. The results of defect feature extraction based on the track inspection data agree well with the geotechnical site investigation results, which demonstrate the feasibility of utilizing multiple track inspection data to identify the typical precipitation-induced subgrade defects.

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Section: Track Inspection Data Analysis Methodsmentioning

confidence: 99%

Section: Time-domain Analysismentioning

confidence: 99%

Feature analysis of precipitation-induced subgrade defects on a high-speed rail ballasted track using multiple track inspection data: A case study

Xiao,

Bai,

Song

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…Differential subgrade settlements contribute to track irregularities and deterioration [2][3][4][5], thereby adversely impacting the dynamic response of the track system during train operations. Various studies [6][7][8][9] suggest that as settlement amplitudes increase, dynamic wheel-rail interactions, car vibrations, and track structure vibrations intensify, posing threats to the comfort and safety of train operations. The stiffness of the track structure is much greater than that of the subgrade.…”

Section: Introductionmentioning

confidence: 99%

Full-Scale Experimental and Field Investigations into Expansion Mechanism of Foamed Polyurethane and its Lifting Behaviors for Repair and Maintenance of Railway Slab Track Systems

Huang,

Su,

Liu

et al. 2024

Polymers

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Excessive settlement of the subgrade seriously reduces the service quality of slab tracks and threatens trains’ running safety. While the utilization of foamed polyurethane is recognized as an effective solution, previous research on its expansion mechanism and its impact on track lifting requires further refinement. Accordingly, a series of full-scale tests, including expansion force tests on foamed polyurethane with diverse qualities and lifting tests of polyurethane grouting with varied qualities on the track structure, have been conducted. The expansion development process of foamed polyurethane is meticulously elucidated, and key expansion parameters are analyzed. Simultaneously, this research explores the lifting behavior of foamed polyurethane grouting under the slab tracks, yielding new insights into essential lifting parameters for track formation repair and maintenance. Based on the experimental data, this study proposes new empirical formulas to comprehensively describe both the expansion mechanism of foam polyurethane and its lifting behavior under the slab tracks. The outcomes of this research offer a new breakthrough for the design of lifting mechanism for maintaining slab track structures through the utilization of foam polyurethane slurry grouting, such as determining the optimal grouting quantity. In addition, these results are instrumental to the evaluation of lifting effects and service life, enhancing the circular economy of railway track systems.

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“…Memon et al [15] developed and validated a web-based cloud system with acceptable accuracy for fault detection on tracks. Ren et al [16] proposed a methodology for the detection of subgrade settlement on railway ballastless track, using vibrations signals, and an algorithm based on support vector machines (SVM), convolutional neural networks (CNN), and particle swarm optimization (PSO). Additionally, Ribeiro et al [17] refer to the fact that the GA can deal with big data, i.e., a significant number of modal parameters.…”

Section: Introductionmentioning

confidence: 99%

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

Traquinho,

Vale,

Ribeiro

et al. 2023

Machines

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Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable.

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Identification method for subgrade settlement of ballastless track based on vehicle vibration signals and machine learning

Cited by 25 publications

References 29 publications

Feature analysis of precipitation-induced subgrade defects on a high-speed rail ballasted track using multiple track inspection data: A case study

Feature analysis of precipitation-induced subgrade defects on a high-speed rail ballasted track using multiple track inspection data: A case study

Full-Scale Experimental and Field Investigations into Expansion Mechanism of Foamed Polyurethane and its Lifting Behaviors for Repair and Maintenance of Railway Slab Track Systems

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

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