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2020 | OriginalPaper | Chapter

Condition Monitoring of Rail Vehicle Suspension Elements: A Machine Learning Approach

Authors: Henrik Karlsson, Alireza Qazizadeh, Sebastian Stichel, Mats Berg

Published in: Advances in Dynamics of Vehicles on Roads and Tracks

Publisher: Springer International Publishing

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Abstract

With an increasing demand for safe and efficient rail transportations with high availability, there is an interest to apply condition based maintenance on railway systems to increase the total system reliability. A condition based maintenance system utilizes data collecting, data processing and decision making to schedule maintenance based on the actual condition of components. In this paper a rail vehicle is simulated at varying operational conditions, and with degraded dampers in the primary and secondary suspension. A large database of simulations is generated and is used to train and test classification algorithms to detect upcoming damper faults, introduced as a fault factor multiplied with the damper coefficients. Frequency response functions between accelerometer signals in the carbody, bogieframes and axles are used as fault indicators, predictors, fed to the classification algorithms. The algorithms are evaluated for a varying number of included frequency response functions, as well as varying operational conditions in the training datasets. The linear Support Vector Machine and 1-Nearest-Neighbour classifier both indicate high capability of correctly classifying damper degradations.
Literature
1.
go back to reference Roberts, C., Goodall, R.: Strategies and techniques for safety and performance monitoring on railways. IFAC Proc. Vol. 42(8), 746–755 (2009) CrossRef Roberts, C., Goodall, R.: Strategies and techniques for safety and performance monitoring on railways. IFAC Proc. Vol. 42(8), 746–755 (2009) CrossRef
2.
go back to reference Alfi, S., Bionda, S., Bruni, S., Gasparetto, L.: Condition monitoring of suspension components in railway bogies. In: 5th IET Conference on Railway Condition Monitoring and Non-Destructive Testing (RCM 2011), pp. 1–6 (2011) Alfi, S., Bionda, S., Bruni, S., Gasparetto, L.: Condition monitoring of suspension components in railway bogies. In: 5th IET Conference on Railway Condition Monitoring and Non-Destructive Testing (RCM 2011), pp. 1–6 (2011)
3.
go back to reference Lv, Y., Wei, X., Guo, S.: Research on fault isolation of rail vehicle suspension system. In: 27th Chinese Control and Decision Conference (2015 CCDC), pp. 929–934 (2015) Lv, Y., Wei, X., Guo, S.: Research on fault isolation of rail vehicle suspension system. In: 27th Chinese Control and Decision Conference (2015 CCDC), pp. 929–934 (2015)
5.
go back to reference MATLAB®: Statistics and Machine Learning Toolbox™, Release R2018b, The MathWorks™ Inc., Natick, Massachusetts, United States MATLAB®: Statistics and Machine Learning Toolbox™, Release R2018b, The MathWorks™ Inc., Natick, Massachusetts, United States
Metadata
Title
Condition Monitoring of Rail Vehicle Suspension Elements: A Machine Learning Approach
Authors
Henrik Karlsson
Alireza Qazizadeh
Sebastian Stichel
Mats Berg
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-38077-9_14

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