Challenges and methodology for pre-processing measured and new rail profiles to efficiently simulate wheel-rail interaction in switches and crossings

Bezin, Yann; Sambo, Bello; Magalhães, H.; Kik, Walter; Megna, Gianluca; Costa, João

doi:10.1080/00423114.2021.2014897

Cited by 10 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [18] took the overall process of the gas reaction as a feature map in data pre-processing to further improve the gas classification of electronic nose algorithms. To improve the classification accuracy, Nickolas et al [19] proposed a data preprocessing method, which combines the correlation-based variable selection technique, synthetic minority oversampling technique, and the imputation technique. Bezin et al [20] proposed an automatic pre-processing method for measured rail data to overcome the challenge of correcting vertical and lateral misalignments of the profile cross-sections.…”

Section: Related Work 21 Data Pre-processingmentioning

confidence: 99%

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

With the development of information technology, a large number of product quality data in the entire manufacturing process is accumulated, but it is not explored and used effectively. The traditional product quality prediction models have many disadvantages, such as high complexity and low accuracy. To overcome the above problems, we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model: radial basis function model optimized by the firefly algorithm with Levy flight mechanism (RBFFALM). First, the new data equalization method is introduced to pre-process the dataset, which reduces the dimension of the data, removes redundant features, and improves the data distribution. Then the RBFFALFM is used to predict product quality. Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

show abstract

Section: Related Work 21 Data Pre-processingmentioning

confidence: 99%

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

show abstract

“…This approach avoids using more than one spring-dashpot assembly to model the track's dynamic properties for different DOFs. A similar approach was considered in the model developed in SIMPACK ® by Carlberger et al [59], and by Bezin et al [60], and the track models discussed by Zhai et al [45].…”

Section: Track Modelmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…The performance of wheel-rail interaction acts as the most important excitation source including wheel out-of-roundness (OOR) (Tao et al, 2021; Zhai et al, 2020), wheel flat (Ling et al, 2015; Ren, 2019), rail corrugation (Wu et al, 2021) as well as rail weld irregularities (Wen et al, 2009). Besides when a vehicle passes through the switches and crossings (S&C, turnout) (Bezin et al, 2022; Wang et al, 2021) or voided sleeper, the dynamic wheel-rail contact force will increase and lead to the vehicle’s derailment. Many researchers have developed various kinds of track models (Pålsson et al, 2021; Sun et al, 2022) to study the influence of turnout and the wear mechanism between the wheel/rail contact (Kisilowski and Kowalik, 2021; Milosevic et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Influence of rail weld irregularity on dynamic stress of bogie frame based on vehicle-track rigid-flexible coupled model

Sun

Lai

Dai

et al. 2022

Journal of Vibration and Control

View full text Add to dashboard Cite

To examine the effects of rail weld irregularity on the dynamic stress of the bogie frame, a numerical model which considers the bogie frame flexibility is established. Field measurements were conducted to validate the reliability of the proposed model and the bogie frame’s modal vibration including the bending deformation of cross beams at 50 Hz can be observed. A comparison between the flexible bogie frame model and the traditional multi-body dynamics model is conducted to investigate the influences of the bogie frame’s flexibility on wheel-rail interactions. Then the numerical model of the rail weld joint is proposed to study the P2 resonance performance and the dynamic stress patterns of the bogie frame. A comparison of influences with different rail weld irregularities and parameter studies are conducted. Then the effects of fastener stiffness on P2 resonance frequencies are discussed. The results show that it is necessary to consider the bogie frame’s flexibility in simulations which will aggravate the wheel-rail vibrations. The short wavelengths in combination with a certain speed could induce the bogie frame’s modal vibration and large track supporting stiffness will threaten the running safety of the vehicle which may also contribute to the formation of wheel out-of-roundness. Besides, the limit values of the rail weld irregularities are discussed to provide a reference for rail surface grinding from different aspects.

show abstract

Challenges and methodology for pre-processing measured and new rail profiles to efficiently simulate wheel-rail interaction in switches and crossings

Cited by 10 publications

References 14 publications

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

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

Influence of rail weld irregularity on dynamic stress of bogie frame based on vehicle-track rigid-flexible coupled model

Contact Info

Product

Resources

About