Anomaly detection using a modified kernel-based tracking in the pantograph-catenary system

A new contactless condition monitoring method is proposed for anomaly detection in the pantograph-catenary system.Kernel-based tracking is modified for a robust tracking of catenary wire.The foreground detection and object tracking are combined for simultaneously arcing detection.The detailed analysis of the trajectory of the contact wire gives useful information to evaluate the pantograph condition. Condition monitoring is very important in railway systems to reduce maintenance costs and to increase the safety. A high power is needed for the movement of the electric train and collection of the current is critical. Faults occurred in the current collection system cause serious damage in the line and disrupt the railway traffic. When a wear occurs on the contact strip, the asymmetries and distortion are generated in supply voltage and current waveforms because of pantograph arcing. Therefore, the monitoring of pantograph-catenary system has been a hot topic in recent years. This paper deals with a method based on kernel-based object tracking for identifying the interaction between pantograph-catenary systems that gives useful information about the problems of catenary-pantograph systems. The method consists of two key components. The first component is based on the kernel based tracking of the contact wire. The contact point between pantograph and catenary is tracked and the obtained positions are saved as a signal. In the other hand, the foreground of each frame is found by using Gaussian mixture models (GMMs). The occurred arcs are detected by combining tracking and foreground detection methods. The second component employs S-transform for analyzing the pantograph problems, which are used to detect the faults occurred on pantograph strip. The experimental results imply that the proposed method is useful to detect burst of arcing, and irregular positioning of the contact wire.

[1]  Z. Zivkovic Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[2]  Ning Zhou,et al.  Pantograph and catenary system with double pantographs for high-speed trains at 350 km/h or higher , 2011 .

[3]  James W. Davis,et al.  Real-time closed-world tracking , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Ali Daadbin,et al.  Development, Testing and Implementation of the Pantograph Damage Assessment System (PANDAS) , 2010 .

[5]  P. M. Keen Monitoring overhead line equipment , 1998 .

[6]  Antonio Fernández Caballero,et al.  Human activity monitoring by local and global finite state machines , 2012 .

[7]  Luca Sani,et al.  Wavelet multiresolution analysis for monitoring the occurrence of arcing on overhead electrified railways , 2003 .

[8]  Alptekin Temizel,et al.  Adaptive mean-shift for automated multi object tracking , 2012 .

[9]  D. Zhang,et al.  Scale and orientation adaptive mean shift tracking , 2012 .

[10]  Xiao-rong Gao,et al.  Study on the Edge Detection and Extraction Algorithm in the Pantographslipper's Abrasion , 2010, 2010 International Conference on Computational and Information Sciences.

[11]  R. Thottappillil,et al.  Pantograph Arcing in Electrified Railways—Mechanism and Influence of Various Parameters—Part II: With AC Traction Power Supply , 2009, IEEE Transactions on Power Delivery.

[12]  Sergey M. Sokolov,et al.  Detecting objects in images in real-time computer vision systems using structured geometric models , 2006, Programming and Computer Software.

[13]  Mehmet Karaköse,et al.  Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection , 2010, Expert Syst. Appl..

[14]  Mauro Tucci,et al.  Use of advanced signal processing techniques for arcing detection on AC pantograph catenary systems , 2011 .

[15]  Amir Hooshang Mazinan,et al.  Applying mean shift, motion information and Kalman filtering approaches to object tracking. , 2012, ISA transactions.

[16]  Luca Sani,et al.  Pantograph-catenary Monitoring Via Wavelet Transform , 2002 .

[17]  Mehmet Karaköse,et al.  A Robust Anomaly Detection in Pantograph-Catenary System Based on Mean-Shift Tracking and Foreground Detection , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[18]  P. Boffi,et al.  Optical Fiber Sensors to Measure Collector Performance in the Pantograph-Catenary Interaction , 2009, IEEE Sensors Journal.

[19]  Antonio Fernández-Caballero,et al.  Human activity monitoring by local and global finite state machines , 2012, Expert Syst. Appl..

[20]  Hao Li,et al.  Unsupervised video anomaly detection using feature clustering , 2012, IET Signal Process..

[21]  Marco Raugi,et al.  Arc detection in pantograph-catenary systems by the use of support vector machines-based classification , 2014 .

[22]  I. Aydin,et al.  A new contactless fault diagnosis approach for pantograph-catenary system , 2012, Proceedings of 15th International Conference MECHATRONIKA.

[23]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[24]  Luca Sani,et al.  Hough transform and thermo-vision for monitoring pantograph-catenary system , 2006 .

[25]  Ferruccio Resta,et al.  Impact of overhead line irregularity on current collection and diagnostics based on the measurement of pantograph dynamics , 2007 .