Robust Parameter Identification for Railway Suspension Systems

Selma Žoljić-Beglerović; Mohammad Ali Golkani; Martin Steinberger; Martin Horn

doi:10.1109/VSS.2018.8460281

Robust Parameter Identification for Railway Suspension Systems

Selma Žoljić-Beglerović, Mohammad Ali Golkani, Martin Steinberger, Martin Horn

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Detecting faults at the right time is crucial both for adequate maintenance and optimal operation of railway vehicles. The increasing complexity of the modern railway system pose a challenge for appropriate fault diagnosis and isolation (FDI). In this paper, a sliding-mode-based parameter identification algorithm is proposed as a solution for the identification of parameters in a simplified railway system. It is assessed on different faulty cases where the accuracy of the estimation and sensitivity to faults are shown, as well as robustness to model uncertainties. The data used for simulations and algorithm evaluation is generated using a validated Multibody Simulation (MBS) model implemented in Simpack, while the numerical simulations are carried out through MATLAB/Simulink.

Original language	English
Title of host publication	15th International Workshop on Variable Structure Systems (VSS)
Pages	432-437
DOIs	https://doi.org/10.1109/VSS.2018.8460281
Publication status	Published - 2018
Event	15th International Workshop on Variable Structure Systems - Graz University of Technology, Graz, Austria Duration: 9 Jul 2018 → 11 Jul 2018

Conference

Conference	15th International Workshop on Variable Structure Systems
Abbreviated title	VSS 2018
Country	Austria
City	Graz
Period	9/07/18 → 11/07/18

Access to Document

10.1109/VSS.2018.8460281

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title = "Robust Parameter Identification for Railway Suspension Systems",

abstract = "Detecting faults at the right time is crucial both for adequate maintenance and optimal operation of railway vehicles. The increasing complexity of the modern railway system pose a challenge for appropriate fault diagnosis and isolation (FDI). In this paper, a sliding-mode-based parameter identification algorithm is proposed as a solution for the identification of parameters in a simplified railway system. It is assessed on different faulty cases where the accuracy of the estimation and sensitivity to faults are shown, as well as robustness to model uncertainties. The data used for simulations and algorithm evaluation is generated using a validated Multibody Simulation (MBS) model implemented in Simpack, while the numerical simulations are carried out through MATLAB/Simulink.",

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AU - Žoljić-Beglerović, Selma

AU - Golkani, Mohammad Ali

AU - Steinberger, Martin

AU - Horn, Martin

PY - 2018

Y1 - 2018

N2 - Detecting faults at the right time is crucial both for adequate maintenance and optimal operation of railway vehicles. The increasing complexity of the modern railway system pose a challenge for appropriate fault diagnosis and isolation (FDI). In this paper, a sliding-mode-based parameter identification algorithm is proposed as a solution for the identification of parameters in a simplified railway system. It is assessed on different faulty cases where the accuracy of the estimation and sensitivity to faults are shown, as well as robustness to model uncertainties. The data used for simulations and algorithm evaluation is generated using a validated Multibody Simulation (MBS) model implemented in Simpack, while the numerical simulations are carried out through MATLAB/Simulink.

AB - Detecting faults at the right time is crucial both for adequate maintenance and optimal operation of railway vehicles. The increasing complexity of the modern railway system pose a challenge for appropriate fault diagnosis and isolation (FDI). In this paper, a sliding-mode-based parameter identification algorithm is proposed as a solution for the identification of parameters in a simplified railway system. It is assessed on different faulty cases where the accuracy of the estimation and sensitivity to faults are shown, as well as robustness to model uncertainties. The data used for simulations and algorithm evaluation is generated using a validated Multibody Simulation (MBS) model implemented in Simpack, while the numerical simulations are carried out through MATLAB/Simulink.

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DO - 10.1109/VSS.2018.8460281

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