Assessment of the current collection properties of a pantograph–catenary system mounted on a train is of great importance. Excessive electric arcing can lead to wear of the system components, and, at the same time, it can be an index of wear status. In this paper we investigate the possibility of detecting arcing events in the pantograph–catenary collection system without the need of additional equipment installed on-board the train. Data that is currently measured and recorded for modern high-speed trains (i.e. voltage and current) are analysed in order to detect and quantify electric arcs and shed light on the current collection quality of the pantograph–catenary system. This work was performed in cooperation with Trenitalia s.p.a. who provided the data it collects on-board high-speed trains in regular passenger service.
Swift M, Aurisicchio G and Pace P. New practices for railway condition monitoring and predictive analysis .In: The IET conference on railway condition monitoring and non-destructive testing, Derby, UK, 29–30 November 2011, pp.1–6 IET.
2.
Betts AI, Hall JH and Keen PM. Condition monitoring of pantographs. In: The international conference on main line railway electrification, 25–28 September 1989, pp.129–133 York, UK: IEEE.
3.
CollinaAFossatiFPapiMRestaF. Impact of overhead line irregularity on current collection and diagnostics based on the measurement of pantograph dynamics. Proc IMechE, Part F: J Rail Rapid Transit2007; 221(4): 547–559.
4.
Elia M, Diana G, Bocciolone M, et al. Condition monitoring of the railway line and overhead equipment through onboard train measurements – an Italian experience. In: The IET conference on railway condition monitoring, Birmingham, UK, 29–30 November 2006, pp.102–107 IET.
5.
Usuda T, Ikeda M and Yamashita Y. Prediction of contact wire wear in high speed railways. In: The ninth world congress on railway research, Lille, France, 22–26 May 2011, pp.1–10.
6.
Daadbin A and Rosinski J. Development, testing and implementation of the pantograph damage assesment system (PANDAS). In: Computers in railways XII. Southampton, UK: WIT Press, 2010, pp.573–578 WIT Press.
7.
BoccioloneMBuccaGCollinaAComolliL. Pantograph–catenary monitoring by means of fibre Bragg grating sensors: results from tests in an underground line. Mech Syst Signal Process2013. 41 (1–2): 226–238.
8.
JutardMFitaireMLe DucE. Moyens d'étude des arcs de rupture du contact pantographe-cateénaire. Revue Générale des Chemin de Fer1989; 108(11): 5–15.
9.
BrunoOLandiAPapiMSaniL. Phototube sensor for monitoring the quality of current collection on overhead electrified railways. Proc IMechE, Part F: J Rail Rapid Transit2001; 215(3): 231–241.
10.
LandiAMenconiLSaniL. Hough transform and thermo-vision for monitoring pantograph-catenary system. Proc IMechE, Part F: J Rail Rapid Transit2006; 220(4): 435–447.
11.
Östlund, S, Gustafsson A, Buhrkall L and Skoglund M. Condition monitoring of pantograph contact strip. In: The fourth IET International Conference on railway condition monitoring, Derby, OK, 18–20 June 2008, pp.1–6. IET.
12.
BuccaGCollinaAManigrassoR. Analysis of electrical interferences related to the current collection quality in pantograph–catenary interaction. Proc IMechE, Part F: J Rail Rapid Transit2011; 225(5): 483–499.
13.
HuangHHChenTH. Development of method for assessing the current collection performance of the overhead conductor rail systems used in electric railways. Proc IMechE, Part F: J Rail Rapid Transit2008; 222(2): 159–168.
14.
BarmadaSLandiAPapiMSaniL. Wavelet multi-resolution analysis for monitoring the occurrence of arcing on overhead electrified railways. Proc IMechE, Part F: J Rail Rapid Transit2003; 217: 177–187.
15.
Barmada S and Tucci M. Use of advanced signal processing techniques for arcing detection on AC pantograph catenary systems. In: PACIFIC 2011 - international conference on pantograph-catenary interaction framework for intelligent control, Amiens, France, 8 December 2011, pp.75–81.
16.
BarmadaSRaugiMTucciMRomanoF. Arc detection in pantograph catenary systems by the use of support vector machines based classification. IET Elect Syst Transport. (in press) DOI: 10.1049/iet-est.2013.0003.
17.
CaiadoJCratoNPeñaD. A periodogram-based metric for time series classification. Comput Stat Data Anal2006; 50: 2668–2684.
18.
HartiganJAManchekAW. Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc, Series C (Appl Stat)1979; 28(1): 100–108.
