On-Board Wheel Profile Classification Based on Vehicle Dynamics - From Physical Effects to Machine Learning
- 148 Downloads
Composite brake blocks often cause hollow worn wheel profiles of freight wagons. The resulting shorter maintenance inspection intervals could be elongated if the wheel profile conditions can be estimated via on-board monitoring systems in service. On the one hand such on-board monitoring algorithms must be very accurate and on the other hand very robust against unknown influencing effects.
This paper shows the process from understanding the physical effects by studying the wheel rail contact conditions of new and worn profiles under several operating conditions up to the application of machine learning algorithms for a robust classification of the wheel profile states. In the first step, the physical effects are investigated by varying different operating conditions with a multi body dynamics model of a freight wagon. In the second step, this generated knowledge is used to find suitable features of measured vehicle response quantities to classify new and worn wheel profiles with machine learning algorithms. In the third step, the accuracy of classification results is analysed for different states of available track information like track irregularities or rail profile conditions.
These investigations show very promising results due to a high accuracy of the developed methodology based on machine learning algorithms. Based on the knowledge of the physical effects, these pre-trained algorithms will be verified with measurement data collected in the Shift2Rail project FR8RAIL II.
This work has been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2Rail through contract n° 730617 (FR8RAIL).
The publication was written at VIRTUAL VEHICLE Research GmbH in Graz, Austria. The authors would like to acknowledge the financial support of the COMET K2 – Competence Centers for Excellent Technologies Programme of the Federal Ministry for Transport, Innovation and Technology (bmvit), the Federal Ministry for Digital, Business and Enterprise (bmdw), the Austrian Research Promotion Agency (FFG), the Province of Styria and the Styrian Business Promotion Agency (SFG). We also want to thank for the support from PJ MESSTECHNIK GMBH (PJM).
- 1.Shahidi, P., Maraini, D., Hopkins, B., Seidel, A.: Railcar bogie performance monitoring using mutual information and support vector machines. In: Annual Conference of the Prognostics and Health Management Society, pp. 1–10 (2015)Google Scholar
- 2.Shahidi, P., Maraini, D., Hopkins, B.: Railcar diagnostics using minimal-redundancy maximum-relevance feature selection and support vector machine classification. Int. J. Progn. Health. Manag. 7, 2153–2648 (2016) Google Scholar