Abstract
A large amount of time-domain waveform images of overvoltage of electric traction network are collected during the on-board test of EMU. The traditional time-domain or frequency-domain analysis and classification method can’t be directly applied to time-domain waveform images. In order to analyze these images, firstly, image preprocessing is carried out. Then, the data set of time-domain waveform images is established. Finally, the CNN (convolutional neural network) classification model is trained based on data set. The results show that the sensitivity of recognition is above 81%, and this method is suitable for the time-domain waveform image of overvoltage of electric traction network.
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