Vertical vibration analysis of a coupled vehicle-container model of a high-speed freight EMU

Xue, Rui; Ren, Zhenwen; Tongbai, Fan; Rakheja, Subhash

doi:10.1080/00423114.2020.1850810

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…They modelled the ballast layers and soil foundation as masses, connected between them through spring-dashpot elements. Xue et al [55] simulated a high-speed freight railway vehicle in SIMPACK ® [32] and considered Kalker's simplified theory of contact (FASTSIM) for computation of the tangential contact forces. Chang et al [33] elaborated a 3D numerical model and found that the deformation to the car body (not only as a rigid body) of a passenger railway vehicle is influenced by railway track damage.…”

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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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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“…Regarding the vehicle modelling, two main approaches can be found in the literature. The first is based on multibody dynamics [13][14][15][16][17][18][19][20][21][22][23], and the second relies on finite element method (FEM) formulations [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…However, in problems where the influence of local modes of vibration is relevant, specifically related to the bending/torsion of vehicle components, the use of these models is required. Typically, the contribution of the local dynamic response is relevant in studies regarding the assessment of passenger comfort [27,33], fatigue-life prediction [29] and cargo stability [26,28].…”

Section: Introductionmentioning

confidence: 99%

“…which, in most situations, presents a nonlinear dynamic behaviour [15,[34][35][36][37][38]. Another issue is the correct modelling of the overload, because in these wagons, the cargo load usually represents a significant portion of the total weight of the vehicle [28]. The high overload/tare mass ratio influences the dynamic behaviour of the wagon, forcing designers to include loaded and unloaded scenarios in their analysis [35].…”

Section: Introductionmentioning

confidence: 99%

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Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios

et al. 2021

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This article presents an efficient methodology for the calibration of a numerical model of a Sgnss freight railway wagon based on experimental modal parameters, namely natural frequencies and mode shapes. Dynamic tests were performed for two distinct static loading configurations, tare weight and current operational overload, under demanding test conditions, particularly during an unloading operation of the train and without disturbing its tight operational schedule. These conditions impose restrictions to the tests, especially regarding the test duration, sensor positioning and system excitation. The experimental setups involve the use of several high-sensitivity accelerometers strategically distributed along with the vehicle platform and bogies in the vertical direction. The modal identification was performed with the application of the enhanced frequency-domain decomposition (EFDD) method, allowing the estimation of 10 natural frequencies and mode shapes associated with structural movements of the wagon platform, which in some cases are coupled with rigid body movements. A detailed 3D FE model of the freight wagon was developed including the platform, bogies, wheelsets, primary suspensions and wheel–rail interface. The model calibration was performed sequentially, first with the unloaded wagon model and then with the loaded wagon model, resorting to an iterative method based on a genetic algorithm. The calibration process allowed the obtainment of the optimal values of eight numerical parameters, including a double estimation of the vertical stiffness of the primary suspensions under the unloaded and loaded static configurations. The results demonstrate that the primary suspensions present an elastic/almost elastic behaviour. The comparison of experimental and numerical responses before and after calibration revealed significant improvements in the numerical models and a very good correlation between the experimental and numerical responses after calibration.

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Railway-Induced Noise and Vibration

Kumar,

Selva Kumar,

Tiwari

et al. 2024

Energy, Environment, and Sustainability

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Vertical vibration analysis of a coupled vehicle-container model of a high-speed freight EMU

Cited by 7 publications

References 26 publications

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

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

Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios

Railway-Induced Noise and Vibration

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