Detecting pantograph-catenary arcing on trains is crucial for ensuring safety in railway operation and maintenance.Most existing detection methods rely on optical instruments to capture pantograph-catenary images,followed by the analysis of these images to identify arc spectra as evidence of arcing occurrences.However,these methods are limited by inadequate visibility in the external envi-ronments of trains,and maintenance access can be challenging.To address these issues,this paper proposed a detection method based on multi-frequency-band characteristics of current signals.First,based on the time-domain and frequency-domain characteristics of arcs,le-veraging theoretically derived,simulated and measured waveforms,the following characteristic components of pantograph-catenary arcs were demonstrated:extremely low-frequency components caused by instantaneous ionization,harmonic components resulting from LC oscillation,and high-frequency components.These characteristic components were then utilized to devise a measurement scheme and da-ta preprocessing algorithm,and historical data were incorporated,leading to the establishment of feature sets.Additionally,a random for-est model was established,with feature vectors as inputs and detection results as outputs.The arcing labels and feature sets provided by 3C equipment were incorporated for training,to develop a classifier enabling real-time arcing detection.Its efficacy was demonstrated through on-board experiments,showcasing a detection precision up to 100％and a recall approximating 98.9％.In addition,the proposed method supports certain extensions for more application scenarios,after training using different event labels provided by users.
维普
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