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  • 1.
    Casanueva, Carlos
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Dirks, Babette
    Trafikverket.
    Tohmmy, Bustad
    Trafikverket.
    Rail Vehicle Dynamics Simulation-based decision support for novel block brake material implementation in Sweden2019In: THE 26TH IAVSD INTERNATIONAL SYMPOSIUM ON DYNAMICS OF VEHICLES ON ROADS AND TRACKS, 12-16 August 2019, Gothenburg, Sweden, 2019Conference paper (Other academic)
    Abstract [en]

    . The application of TSI Noise in Sweden needs decision support that can objectively state system-wide benefits and disadvantages, as there are issues with the introduction of novel block brakes: reduced braking performance and increased equivalent conicity. The Roll2Rail Universal Cost Model (UCM) is used to analyse Life Cycle Cost (LCC), as it was also conceived so that it could be used within the decision making processes of infrastructure managers. The simulated characteristics are mainly track Rolling Contact Fatigue (RCF) due to the worsened dynamic train-track interaction.

    Download full text (pdf)
    fulltext
  • 2. Giossi, Rocco Libero
    et al.
    Persson, Rickard
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Stichel, Sebastian
    KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Gain Scaling for Active Wheelset Steering on Innovative Two-Axle Vehicle2020In: The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks: Advances in Dynamics of Vehicles on Roads and Tracks, 2020Conference paper (Refereed)
    Abstract [en]

    Within the Shift2Rail project Run2Rail, an innovative single axle running gear with only one suspension step is proposed. A composite material frame shall be used both as structural and as suspension element. To improve curving performance active wheelset steering control is introduced. The selected control aims to minimize the longitudinal creepage by controlling the lateral wheelset position on the track. A two-axle vehicle is created in the MBS program SIMPACK and co-simulation is established with Simulink. The control strategy used is a simple PID control. A set of run cases with different curves and speeds is selected to verify the performance. The control gain optimal for high non-compensated lateral acceleration (NLA) tends to produce unstable results for low speeds. Control gain scaling is introduced based on vehicle speed and online estimation of the curvature. The proposed gain scheduling approach maintains the simple control formulation still solving the instability problem. Gain scheduling allows use of optimal control gains for all combinations of curve radii and vehicle speed and thereby taking the full advantage that the active wheelset steering brings to a vehicle with single axle running gears.

  • 3.
    Giossi, Rocco Libero
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Stichel, Sebastian
    KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.
    Persson, Rickard
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Gain Scaling for Active Wheelset Steering on Innovative Two-Axle Vehicle2020In: Lecture Notes in Mechanical Engineering, 2020Conference paper (Refereed)
    Abstract [en]

    Within the Shift2Rail project Run2Rail, an innovative single axle running gear with only one suspension step is proposed. A composite material frame shall be used both as structural and as suspension element. To improve curving performance active wheelset steering control is introduced. The selected control aims to minimize the longitudinal creepage by controlling the lateral wheelset position on the track. A two-axle vehicle is created in the MBS program SIMPACK and co-simulation is established with Simulink. The control strategy used is a simple PID control. A set of run cases with different curves and speeds is selected to verify the performance. The control gain optimal for high non-compensated lateral acceleration (NLA) tends to produce unstable results for low speeds. Control gain scaling is introduced based on vehicle speed and online estimation of the curvature. The proposed gain scheduling approach maintains the simple control formulation still solving the instability problem. Gain scheduling allows use of optimal control gains for all combinations of curve radii and vehicle speed and thereby taking the full advantage that the active wheelset steering brings to a vehicle with single axle running gears. 

  • 4.
    KULKARNI, ROHAN
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Qazizadeh, Alireza
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Berg, Mats
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Stichel, Sebastian
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    FAULT DETECTION AND ISOLATION METHOD FOR VEHICLE RUNNING INSTABILITY FROM VEHICLE DYNAMICS RESPONSE USING MACHINE LEARNING2019In: Proceedings of 11th International Conference on Railway Bogies and Running Gears (BOGIE'19) / [ed] Prof. István ZOBORY, Budapest, 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, a Fault Detection and Isolation (FDI) method is proposed for monitoring the vehicle running stability in a high-speed railway bogie. The objective is to detect and isolate the different faults of bogie components which are critical to vehicle stability, especially degraded yaw dampers and high equivalent conicity caused by wheel wear. The proposed method has two steps; firstly, signal features sensitive to the characteristics of running instability are extracted based on frequency domain and time domain analysis of lateral accelerations of bogie frame and axlebox; then these features along with vehicle speed are fed into machine learning based fault classifiers. The supervised machine learning based fault classifier are trained to identify the cause of observed running instability among yaw damper degradation and wheel-rail profile pair with high equivalent conicity. The Support Vector Machine (SVM) classifier with Linear and Gaussian kernels are trained by k-fold crossvalidation method and the hyperparameters are optimized with a bayesian optimization algorithm to minimize the classification error. These fault classifiers are trained and tested with an extensive database generated from numerical experiments performed by multibody simulation (MBS) software. The performance of Linear and Gaussian SVM fault classifiers is compared with each other to identify the best performing classifier. The results underline the ability of machine learning based fault classifiers to be used for FDI of vehicle running instability and outline the possibility of detecting and isolating bogie faults critical to the vehicle stability based on onboard measurement of vehicle dynamic response.

  • 5.
    KULKARNI, ROHAN
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Qazizadeh, Alireza
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Berg, Mats
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Stichel, Sebastian
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    FAULT DETECTION AND ISOLATION METHOD FOR VEHICLE RUNNING INSTABILITY FROM VEHICLE DYNAMICS RESPONSE USING MACHINE LEARNING2019In: Proceedings of 11th International Conference on Railway Bogies and Running Gears (BOGIE'19) / [ed] Prof. István ZOBORY, Budapest, 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, a Fault Detection and Isolation (FDI) method is proposed for monitoring the vehicle running stability in a high-speed railway bogie. The objective is to detect and isolate the different faults of bogie components which are critical to vehicle stability, especially degraded yaw dampers and high equivalent conicity caused by wheel wear. The proposed method has two steps; firstly, signal features sensitive to the characteristics of running instability are extracted based on frequency domain and time domain analysis of lateral accelerations of bogie frame and axlebox; then these features along with vehicle speed are fed into machine learning based fault classifiers. The supervised machine learning based fault classifier are trained to identify the cause of observed running instability among yaw damper degradation and wheel-rail profile pair with high equivalent conicity. The Support Vector Machine (SVM) classifier with Linear and Gaussian kernels are trained by k-fold crossvalidation method and the hyperparameters are optimized with a bayesian optimization algorithm to minimize the classification error. These fault classifiers are trained and tested with an extensive database generated from numerical experiments performed by multibody simulation (MBS) software. The performance of Linear and Gaussian SVM fault classifiers is compared with each other to identify the best performing classifier. The results underline the ability of machine learning based fault classifiers to be used for FDI of vehicle running instability and outline the possibility of detecting and isolating bogie faults critical to the vehicle stability based on onboard measurement of vehicle dynamic response.

  • 6.
    Rosa, Anna De
    et al.
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Kulkarni, Rohan
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Qazizadeh, Alireza
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Berg, Mats
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Gialleonardo, Egidio Di
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Faccinetti, Alan
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Bruni, Stefano
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms2020In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017Article in journal (Refereed)
    Abstract [en]

    In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics.

    Download full text (pdf)
    Accepted_Version
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