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A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout.

Authors :
Chen Chen
Xingqiu Li
Kai Huang
Zhongwei Xu
Meng Mei
Source :
CMES-Computer Modeling in Engineering & Sciences; 2023, Vol. 136 Issue 1, p471-485, 15p
Publication Year :
2023

Abstract

Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train's safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a twodimensional image by data pre-processing and 2D representation. Next, a binary classification model based on the convolutional autoencoder is developed to implement fault detection. The profile and structure information can be captured by processing data as images. The performance of our method is evaluated and tested on real-world operational current data in themetro stations. Experimental results show that the proposedmethod achieves better performance, especially in terms of error rate and specificity, and is robust in practical engineering applications. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15261492
Volume :
136
Issue :
1
Database :
Complementary Index
Journal :
CMES-Computer Modeling in Engineering & Sciences
Publication Type :
Academic Journal
Accession number :
161262825
Full Text :
https://doi.org/10.32604/cmes.2023.024033