Development of train ride comfort prediction model for railway slab track system

Sadeghi, Javad; Rabiee, Siamak; Khajehdezfuly, Amin

doi:10.1590/1679-78256237

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…In this section, the results obtained from this research are compared with the conventional methods currently available in the literature for calculating the length of transition curves. 26,27 For this purpose, the empirical methods and equations shown in Table 1 were used. As shown in Table 1, some of the equations for calculating the TCL are functions of the curve super-elevation and the vehicle allowable speed.…”

Section: Evaluation Of Current Methodsmentioning

confidence: 99%

Investigation on optimum lengths of railway clothoid transition curves based on passenger ride comfort

Sadeghi

Rabiee

Felekari

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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Transition curves are considered as inevitable parts of railways. As they tend to bend the train’s course of movement, the passengers ride comfort and safety are strongly challenged. Although many investigations have performed on the dynamic behavior characteristics of transition curves, only a few studies have focused on the optimum transition length based on the ride comfort index. In response to this need, a vehicle-track dynamic model was developed. The results obtained from model were compared with those available in the literature to prove the validity of the model. Through a parametric study, the dynamic responses of a vehicle passing through the transition curves was studied using the model developed in this study. The ride comfort index was obtained using the dynamic response of the vehicle. The lateral jerk, vertical acceleration, and rolling velocity were considered as the dynamic response of the vehicle. The optimum transition curve length for various configurations of curve radius and super-elevation magnitudes were investigated based on the obtained ride comfort.

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Section: Evaluation Of Current Methodsmentioning

confidence: 99%

Investigation on optimum lengths of railway clothoid transition curves based on passenger ride comfort

Sadeghi

Rabiee

Felekari

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…33 Sadeghi et al have investigated the effect of structural conditions on track roughness, 34 and developed a track geometry assessment technique that incorporates rail cant factor based on modelling of rail-wheel contact using ADAMS/Rail program, 35 as well as development of a train ride comfort (TRC) in railway slab tracks that has considered the slab track and rolling stock influencing parameters. 36 Other recent research has estimated the track geometry roughness of Shinkansen high-speed tracks using car-body motion 37 and in-service railway vehicles. 38 The first part of this paper applies a weighted filtering method as per ISO 2631-1997 standard to determine the locations where highly impact the ride quality, to investigate the influence of type of track features and speed on ride quality, and to assess the change of ride quality during the five months of study.…”

Section: Introductionmentioning

confidence: 99%

“…have investigated the effect of structural conditions on track roughness, 34 and developed a track geometry assessment technique that incorporates rail cant factor based on modelling of rail-wheel contact using ADAMS/Rail program, 35 as well as development of a train ride comfort (TRC) in railway slab tracks that has considered the slab track and rolling stock influencing parameters. 36 Other recent research has estimated the track geometry roughness of Shinkansen high-speed tracks using car-body motion 37 and in-service railway vehicles. 38 …”

Section: Introductionmentioning

confidence: 99%

Use of measured accelerations from a passenger rail car to evaluate ride quality and track roughness – A case study

Abdulrazagh

Hendry

Gül

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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Increasing traffic and speeds on passenger rail lines, and a short season for maintenance work, have motivated the industry to find new methods to assess the condition of existing infrastructure and determine where upgrades are required. In this study, acceleration data from the car body and axle boxes of a revenue car over 92 km of a Canadian passenger rail route in Ontario were collected for two purposes: first, to apply weighted filtering method according to ISO 2631-1997 standard as a technique to determine the locations which highly impact the ride quality and to investigate the effect of type of track features and speed on the ride quality; second, a new analytical method called the envelope of acceleration was applied to use the recorded accelerations to evaluate the alignment and surface roughness along the track. Since the alignment and surface roughness values are always positive and are calculated over a specified length (e.g. 9.5 m, 18.9 m, 38 m) an envelope technique was employed which uses spline interpolations over local maxima of the absolute magnitude of accelerations at every separated n samples corresponding to best fit with track roughness. The regression analysis between the envelope of accelerations and alignment and surface roughness presented a meaningful correlation and showed the applied method is a promising analytical technique to indicate rough sections of the track. The limitations to the application of envelope of acceleration are also discussed.

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“…In fact, some of them use advanced predictive algorithms such as neural networks and statistical correlations to predict this variable [7,8]. In addition, other studies address the prediction of comfort values in high-speed trains using multibody dynamic models, including information about the track geometry and a simplified model of the rolling stock [9,10]. However, there is not any study that merges these two ideas together in a mathematical and engineering sense.…”

Section: Introductionmentioning

confidence: 99%

Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

et al. 2021

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Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ comfort is directly related to the accelerations received through the car-body of the train. For this reason, suspension and damping components must be maintained in perfect condition, assuring high levels of comfort quality. An early detection of any potential failure in these systems derives in a better maintenance inspections’ planification and in a more sustainable approach to the whole train maintenance strategy. In this paper, an optimized model based on neural networks is trained in order to predict lateral car-body accelerations. Comparing these predictions to the values measured on the train, a normal characterisation of the lateral dynamic behaviour can be determined. Any deviation from this normal characterisation will imply a comfort loss or a potential degradation of the suspension and damping components. This model has been trained with a dataset from a specific train unit, containing variables recorded every second during the year 2017, including lateral and vertical car-body accelerations, among others. A minimum average error of 0.034 m/s2 is obtained in the prediction of lateral car-body accelerations. This means that the average error is approximately 2.27% of the typical maximum estimated values for accelerations in vehicle body reflected in the EN14363 for the passenger coaches (1.5 m/s2). Thus, a successful model is achieved. In addition, the model is evaluated based on a real situation in which a passenger noticed a lack of comfort, achieving excellent results in the detection of atypical accelerations. Therefore, as it is possible to measure acceleration deviations from the standard behaviour causing lack of comfort in passengers, an alert can be sent to the operator or the maintainer for a non-programmed intervention at depot (predictive maintenance) or on board (prescriptive maintenance). As a result, a condition-based maintenance (CBM) methodology is proposed to avoid comfort degradation that could end in passenger complaints or speed limitation due to safety reasons for excessive acceleration. This methodology highlights a sustainable maintenance concept and an energy efficiency strategy.

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Development of train ride comfort prediction model for railway slab track system

Cited by 7 publications

References 44 publications

Investigation on optimum lengths of railway clothoid transition curves based on passenger ride comfort

Investigation on optimum lengths of railway clothoid transition curves based on passenger ride comfort

Use of measured accelerations from a passenger rail car to evaluate ride quality and track roughness – A case study

Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

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