Beyond Condition-Monitoring: Comparing Diagnostic Events with Word Sequence Kernel for Train Delay Prediction



Published Jul 5, 2016
Wan-Jui Lee David M.J. Tax Robert P.W. Duin


In the modern trains operated by the Dutch Railways (Nederlandse Spoorwegen) in the Netherlands, there are on-board train management systems continuously monitoring the conditions of various train modules such as traction, climate, brake electronics and so forth. When an abnormal or particular situation occurs, the system will generate and store an event log on the local disk or on a remote disk using wireless data communications. These diagnostic events might give an indication of the train condition, and currently critical events are selected by business rules to give alarms on failure or malfunction to the control room. To give a better prediction on the trains status based on the condition monitoring data, sequences of diagnostic events instead of individual critical events are analyzed in this work. Moreover, train delays instead of train failures are used as targets for providing more insight on the degeneration behavior of trains. We have adopted the word sequence kernel for learning the similarity between all sequence pairs, where each diagnostic event is considered as a word. To include multi-length word interpretations, we propose to combine the word sequence kernels of various lengths, where length=1 means one word is matched, length=2 means two words are matched, and so on. A kernel machine or similarity-based model can be learned directly on this combined word sequence kernel. The experimental results demonstrate that combining word sequence kernels of different lengths can bring a richer description to similarity measurements and gives better prediction performance.

How to Cite

Lee, W.-J., Tax, D. M., & Duin, R. P. (2016). Beyond Condition-Monitoring: Comparing Diagnostic Events with Word Sequence Kernel for Train Delay Prediction. PHM Society European Conference, 3(1).
Abstract 144 | PDF Downloads 145



Data-driven Methods, Preventive and predictive maintenance, Condition-based Monitoring

de Vos, J. I. A., & van Dongen, L. A. M. (2015). Performance Centered Maintenance as a Core Policy in Strategic Maintenance Control. Procedia CIRP, vol. 38, pp. 255–258, 2015.
Eker, O. F., Camci, F., & Jennions, I. K. (2014). A Similarity-based Prognostics Approach for Remaining Useful Life Prediction. Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014.
Jiang, J., Huisman, B., & Dignum, V. (2012). Agent-based multi-organizational interaction design: A case study of the dutch railway system. IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 2, pp. 196–203, 2012.
Poot-Geertman, P., Huisman, B., & van Rijn, C. F. H. (2015). Application of a Maintenance Engineering Decisin Method for Railway Operation: Managing Fleet Performance, Cost, and Risk. Safety and Reliability of Complex Engineered Systems: ESREL 2015, pp. 1863–1870, 2015.
Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., & Watkins, C. (2002). Text classification using string kernels. Journal of Machine Learning Research, vol. 2, pp. 419–444.
Cancedda, N., Gaussier, E., Goutte, C., & Renders, J. (2003). Word-Sequence Kernels. Journal of Machine Learning Research, pp. 1059-1082.
Vapnik, V., (1995). The Nature of Statistical Learning Theory. In Louis Redding & Rajkumar Roy (Eds.), New York: Springer.
Lanckriet, G. R. G., Cristianini, N., Bartlett, P., Ghaoui, L. E., & Jordan, M. I. (2004). Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research, vol. 5, pp. 27-72.
Technical Papers