Feature Extraction for Track Section Status Classification Based on UGW Signals
Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain tr...
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doaj-a1a8f7782d1d46ad9d70f7264c1f168b2020-11-25T02:27:32ZengMDPI AGSensors1424-82202018-04-01184122510.3390/s18041225s18041225Feature Extraction for Track Section Status Classification Based on UGW SignalsLei Yuan0Yuan Yang1Álvaro Hernández2Lin Shi3Electronics Department, Xi’an University of Technology, Xi’an 710048, ChinaElectronics Department, Xi’an University of Technology, Xi’an 710048, ChinaElectronics Department, University of Alcala, Alcalá de Henares, Madrid 28805, SpainElectronics Department, Xi’an University of Technology, Xi’an 710048, ChinaTrack status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works.http://www.mdpi.com/1424-8220/18/4/1225track status classificationultrasonic guided wave (UGW)feature extractiontemporal and spatial dependenciesdeep learning algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Yuan Yuan Yang Álvaro Hernández Lin Shi |
spellingShingle |
Lei Yuan Yuan Yang Álvaro Hernández Lin Shi Feature Extraction for Track Section Status Classification Based on UGW Signals Sensors track status classification ultrasonic guided wave (UGW) feature extraction temporal and spatial dependencies deep learning algorithm |
author_facet |
Lei Yuan Yuan Yang Álvaro Hernández Lin Shi |
author_sort |
Lei Yuan |
title |
Feature Extraction for Track Section Status Classification Based on UGW Signals |
title_short |
Feature Extraction for Track Section Status Classification Based on UGW Signals |
title_full |
Feature Extraction for Track Section Status Classification Based on UGW Signals |
title_fullStr |
Feature Extraction for Track Section Status Classification Based on UGW Signals |
title_full_unstemmed |
Feature Extraction for Track Section Status Classification Based on UGW Signals |
title_sort |
feature extraction for track section status classification based on ugw signals |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-04-01 |
description |
Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works. |
topic |
track status classification ultrasonic guided wave (UGW) feature extraction temporal and spatial dependencies deep learning algorithm |
url |
http://www.mdpi.com/1424-8220/18/4/1225 |
work_keys_str_mv |
AT leiyuan featureextractionfortracksectionstatusclassificationbasedonugwsignals AT yuanyang featureextractionfortracksectionstatusclassificationbasedonugwsignals AT alvarohernandez featureextractionfortracksectionstatusclassificationbasedonugwsignals AT linshi featureextractionfortracksectionstatusclassificationbasedonugwsignals |
_version_ |
1724842520466161664 |