Early Warnings for failing Train Axle Bearings based on Temperature

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 2, 2017
M.F.E. Peters

Abstract

By studying the temperature behavior of axle bearings both statistically and physically, the research and development (R&D) department of Netherlands Railways (NS) has successfully developed and implemented a health monitoring system for bearings. In an early stage of degradation, temperature deviations are detected and the level of severity of the degradation is identified through a decision tree. This method enables us to detect bearing failures one to three months earlier than any other method in use, in more than half of the cases. Different handling scenarios per type of temperature behavior have been designed in a way that minimizes impact on train service.

How to Cite

Peters, M. (2017). Early Warnings for failing Train Axle Bearings based on Temperature. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2451
Abstract 208 | PDF Downloads 486

##plugins.themes.bootstrap3.article.details##

Keywords

implementation, decision tree, bearing fault detection, temperature, PHM in Railways, rule-based classifier

References
Apallius de Vos, J.I., & van Dongen, L.A.M., (2015), Performance Centered Maintenance as a core policy in strategic maintenance control. Proceedings of the Fourth International Conference on Through-life Engineering Services doi:10.1016/j.procir.2015.08.016
Vale, C., Bonifácio, C., Seabra, J., Calçada, R., Mazzino, N., Elisa, M., Terribile, S., Anguita, D., Fumeo, E., Saborido, C., Vanhonacker, T., De Donder, E., Laeremans, M., Vermeulen, F., & Grimes, D. (2016) Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project. Proceedings of the sixth Transport Research Arena. April 18-21. doi:10.1016/j.trpro.2016.05.313
Dasgupta, A., & Pecht, M., (1991) Material Failure Mechanisms and Damage models. IEEE Transactions on Reliability, vol. 40, no. 5, pp. 531-536.
Rasoul Safavian, S., & Landgrebe, D., (1991) A Survey of Decision Tree Classifier Methodology. IEEE Transactions on Systems, Man, and Cybernetics, vol. 21, no. 3, pp. 660-674.
Lee, W.J., Ouyang, Ch.Sh., & Lee, Sh.J., (2002) Constructing Neuro-Fuzzy Systems with TSK Fuzzy Rules and Hybrid SVD-Based Learning. Proceedings of the IEEE International Conference on Fuzzy Systems, Feb 2002
Kunche, S., Chen, Ch., & Pecht, M., (2012) A review of PHM system’s architectural frameworks MFPT 2012: Proceedings of the Prognostics and Health Management Solutions Conference, April 24-26, Dayton, OH, 2012
Section
Technical Research Papers