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International Journal of Railway Technology
ISSN 2049-5358
IJRT, Volume 3, Issue 4, 2014
Hierarchical Clustering applied to Measured Data Relative to Pantograph-Catenary Systems as a Predictive Maintenance Tool
S. Barmada1, M. Tucci1 and F. Romano2

1DESTEC, University of Pisa, Italy
2Trenitalia s.p.a., Italy

Full Bibliographic Reference for this paper
S. Barmada, M. Tucci, F. Romano, "Hierarchical Clustering applied to Measured Data Relative to Pantograph-Catenary Systems as a Predictive Maintenance Tool", International Journal of Railway Technology, 3(4), 23-41, 2014. doi:10.4203/ijrt.3.4.2
Keywords: railway systems, pantograph-catenary system, predictive maintenance, time series clustering, electric arc, signal processing.

Abstract
A hierarchical clustering algorithm, based on fuzzy c-means, is developed and applied to a set of measured data (voltages and currents) collected by the pantograph of high speed trains in order to detect the presence of electric arcs and classify their magnitude. During the test runs, the electric arc has been recorded by a photosensitive device, and is used in this analysis to evaluate the effectiveness of the clustering procedure.

The results show that these obtained clusters are effectively related to the presence and magnitude of electric arcs and that the technique could be used as a tool for preventive maintenance.

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