A New Fault Detection Algorithm for EMUs based on Deep Learning

DOI

10.2991/mecae-17.2017.49How to use a DOI?

Keywords

Fault detection; Convolution neural networks; Deep learning.

Abstract

Health monitoring is an important task for high-speed railway EMUs. Traditional methods for detection have the problem of low detection rate or high false alarm. In this paper, a new fault detection algorithm is proposed based on deep learning. A region proposal network is applied and a network is trained based on EMU images. Initial experiments show that the proposed network can achieve 95.67% accuracy in detection, which the speed of 0.1-0.2 second per image.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhi Liu
AU  - Yanru Sun
PY  - 2017/03
DA  - 2017/03
TI  - A New Fault Detection Algorithm for EMUs based on Deep Learning
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 264
EP  - 267
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.49
DO  - 10.2991/mecae-17.2017.49
ID  - Liu2017/03
ER  -