On-line Identification Method of Pantograph Anomaly Based on Feature Analysis

Abstract

Inspection of pantograph state of EMU is one of the basic tasks of vehicle daily maintenance. There are three common inspection methods, including manual visual inspection, fixed-point image detection and on-board image detection. Due to the small number of pantograph fault samples that can be obtained, the present pantograph anomaly identification algorithms based on deep learning perform poorly. Therefore, this paper proposes an on-line recognition method based on feature analysis and independent of the number of fault samples. Visual features of the visible light images of pantograph which collected in real time through 3C device of EMU are used to identify abnormal states of pantograph. Firstly, the light intensity of the operating environment is judged by the image gray feature, and the region of interest (ROI) of the pantograph is segmented by the tracking matching algorithm. Then, the object features in the ROI of each frame such as threshold, contour thickness and tilt angle are extracted to detect abnormal states respectively, including sheep angle missing, front carbon sliding plate defect and bow head tilt, etc. Finally, the characteristic rules of pantograph anomaly determination are designed to realize the identification of abnormal state of pantograph. Test results show that the method and system proposed in this paper can detect abnormal states of the pantograph, such as missing horns, front carbon sliding blocks and bow head tilting, in both day and night environments. The average detection rate of defects is greater than 98%, which shows the method has great stability and practicability.

Keywords

• pantograph anomaly
• on-line identification
• feature analysis
• adaptive
• tracking matching