On-board monitoring for smart assessment of railway infrastructure: A systematic review

Hoelzl, Cyprien; Dertimanis, Vasilis K.; Landgraf, Matthias; Ancu, Lucian; Zurkirchen, Marcel; Chatzi, Eleni

doi:10.1016/b978-0-12-817784-6.00015-1

Cited by 15 publications

(8 citation statements)

References 50 publications

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“…The rail-sleeper and sleeper-ground contacts are realised via force elements of linear stiffness and linear damping. The mm-accurate track geometric layout and excitation, used as an input for the simulation, are obtained from measurements of a diagnostic vehicle [3].…”

Section: Simpack Simulation Modelmentioning

confidence: 99%

“…Ensuring the quality and safety of rail transport requires monitoring and evaluating the condition of rail infrastructure. Specifically, for the track system, continuous monitoring can identify faults at an early stage [1,2,3,4], preventing severe damage to track and vehicle components, reducing rolling noise, minimizing passengers' discomfort due to excessive vibrations, and, ultimately, enhancing the overall safety of rail transport [5,6]. This degradation of tracks mainly owes to vehicle load cycles and vehicle-track interaction [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Railway Vehicle Parameter Tuning using Markov-Chain Monte Carlo Bayesian updating

Hoelzl,

Keller,

Simpson

et al. 2024

J. Phys.: Conf. Ser.

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Understanding the dynamics of the interaction between railway vehicles and tracks is essential for forecasting vehicle and track conditions and performing maintenance actions to preserve the safety of railway infrastructure. In this work, physics-based models are deployed to predict the dynamic response of railway vehicles to track alignment and irregularities. Such models comprise a large number of parameters that need to be validated and possibly tuned, a task often accomplished on the basis of expert knowledge or trial-and-error without necessarily reflecting the condition of the operating system as-is. Therefore, model uncertainties persist due to assumptions on the actual system behavior, as well as due to varying operational parameters (e.g., wheel profile, rail profile, rail moisture). To improve modelling accuracy, this study adopts a multi-body vehicle model, realized in SIMPACK software, and performs parameter tuning based on on-board measurement data (accelerations and forces) from an instrumented tilting train, which regularly traverses the Swiss Federal Railways (SBB) network. The pertinent vehicle model is optimized in terms of the interaction forces at the wheel-rail contact using a Bayesian model updating approach, relying on Markov-Chain Monte Carlo (MCMC). Specifically, the MCMC method is applied to estimate both optimal vehicle parameters and their uncertainty by minimizing the mean squared error between simulated and measured contact forces, leading to an improved vehicle twin.

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Section: Simpack Simulation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Railway Vehicle Parameter Tuning using Markov-Chain Monte Carlo Bayesian updating

Hoelzl,

Keller,

Simpson

et al. 2024

J. Phys.: Conf. Ser.

Self Cite

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“…Despite refecting a high degree of fdelity, these techniques entail high costs and efort, while they cannot cover extended portions of rail infrastructure on a frequent basis. As a result, mobile sensing approaches have been explored as an alternative [4].…”

Section: Introductionmentioning

confidence: 99%

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

Stoura,

Dertimanis,

Hoelzl

et al. 2023

Structural Control and Health Monitoring

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According to the International Union of Railways, railway networks count more than one million kilometers of tracks worldwide, a number that is to rise further as the goal is to promote rail transportation as a sustainable means to face the challenge of increased mobility. However, such a vast expansion further necessitates efficient and reliable infrastructure monitoring schemes able to guarantee the quality and safety of rail transportation. Traditional monitoring approaches, relying on visual inspection and portable measuring devices, cannot rise to the task as they do not allow for continuous inspection of extended portions of rail infrastructure. Therefore, mobile monitoring methodologies based on dedicated diagnostic vehicles have emerged as an alternative. Despite revolutionizing traditional monitoring methods, such vehicles are usually expensive and can only operate under the suspension of regular rail service. In this work, we propose an alternative approach for mobile sensing of railway infrastructure based on on-board monitoring data collected from low-cost vibration sensors, e.g., accelerometers, which can be mounted on in-service trains. Specifically, we focus on identifying the roughness profile of the tracks and propose a fusion of reduced-order vehicle models with a Bayesian inference approach for joint input-state estimation. To enhance the inference, we opt for a prior updating of the vehicle model parameters on the basis of an unscented Kalman filter and available measurements from a diagnostic vehicle. The key contributions of this work are (i) the consideration of the dynamic interaction between trains and tracks, which is usually ignored in rail roughness estimation, (ii) the adoption of reduced train vehicle models that decrease the computational effort of the identification task, (iii) the updating of the vehicle parameters to account for inconsistencies in the model used, and (iv) the application of the proposed methodology to actual acceleration measurements collected from a diagnostic vehicle of the Swiss Federal Railways network.

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“…To deal with these problems, railway operators are investing in new methods for monitoring the track status of the railway network. One possible way is to apply on-board monitoring (OBM) techniques for monitoring the track status ( 3 ). The main concept for the OBM technique is to use commercial trains, instead of TRVs, for monitoring the track status.…”

mentioning

confidence: 99%

Developing and Extending Status Prediction Models for Railway Tracks Based on On-Board Monitoring Data

Yan

Costa

Corman

2023

Transportation Research Record: Journal of the Transportation R

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Assigning inspection trains to monitor track quality is a standard procedure for maintaining railway system safety. The main challenges lie in lacking time and resources to perform the inspections because of the increasing traffic nowadays. To overcome these challenges, many consider adopting the on-board monitoring (OBM) technique for performing the inspections. This technique assigns commercial trains, instead of traditional track recording vehicles (TRVs), to monitor the track status, allowing railway operators to perform more inspections without affecting the traffic and using expensive inspection trains as well. However, compared with TRV data, the new OBM data are of lower data quality and have fewer features, although they can be recorded more frequently. Therefore, new methods should be developed for effectively applying the new data. This study develops four models, namely the linear regression model, Markov model, ordinary Kriging model, and Kalman filter model, for predicting the track status based on the OBM data. Data collected from the Switzerland railway network are used for verifying the models. Results show that the proposed models can effectively predict the degradation of the track status in different ways and, therefore, assist railway operators in scheduling maintenance tasks.

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On-board monitoring for smart assessment of railway infrastructure: A systematic review

Cited by 15 publications

References 50 publications

Data-Driven Railway Vehicle Parameter Tuning using Markov-Chain Monte Carlo Bayesian updating

Data-Driven Railway Vehicle Parameter Tuning using Markov-Chain Monte Carlo Bayesian updating

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

Developing and Extending Status Prediction Models for Railway Tracks Based on On-Board Monitoring Data

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