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Self-attention and convolutional feature fusion for real-time intelligent fault detection of high-speed railway pantographs
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Xufeng LI1, Jien MA1, Ping TAN2, Lanfen LIN3, Lin QIU1, Youtong FANG1. Self-attention and convolutional feature fusion for real-time intelligent fault detection of high-speed railway pantographs[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Self-attention and convolutional feature fusion for real-time intelligent fault detection of high-speed railway pantographs",
author="Xufeng LI1, Jien MA1, Ping TAN2, Lanfen LIN3, Lin QIU1, Youtong FANG1",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2400549"
}
%0 Journal Article
%T Self-attention and convolutional feature fusion for real-time intelligent fault detection of high-speed railway pantographs
%A Xufeng LI1
%A Jien MA1
%A Ping TAN2
%A Lanfen LIN3
%A Lin QIU1
%A Youtong FANG1
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2400549
TY - JOUR
T1 - Self-attention and convolutional feature fusion for real-time intelligent fault detection of high-speed railway pantographs
A1 - Xufeng LI1
A1 - Jien MA1
A1 - Ping TAN2
A1 - Lanfen LIN3
A1 - Lin QIU1
A1 - Youtong FANG1
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2400549
Abstract: Currently, most trains are equipped with dedicated cameras for capturing pantograph videos. Pantographs are core to the high-speed-railway pantograph-catenary system and their failure directly affects the normal operation of high-speed trains. However, given the complex and variable real-world operational conditions of high-speed railways, there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video. Hence, it is of paramount importance to maintain real-time monitoring and analysis of pantographs. Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs, utilizing a fusion of self-attention and convolution features. We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies. Compared with traditional methods, this approach achieves high recall and accuracy in pantograph recognition, accurately pinpointing issues like discharge sparks, pantograph horns, and carbon pantograph-slide malfunctions. After experimentation and validation with actual Electric Multiple Units train-surveillance videos, our algorithmic model demonstrates real-time, high-accuracy performance even under complex operational conditions.
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