Computational Technology Resources - IJRT

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.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to IJRT
purchase this issue (price £70 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	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 doi:10.4203/ijrt.3.4.2 purchase the full-text of this paper 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. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to IJRT purchase this issue (price £70 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions