Paper Info
Title
Intelligent Inspection of Railways Infrastructure and Risks Estimation by Artificial Intelligence Applied on Noninvasive Diagnostic Systems
Abstract
The proposed work focused on a methodological approach to perform inspections by means of non-invasive diagnostic devices, based on Ground Penetrating Radar (GPR), laser scanner and standalone temperature sensor technologies. The data acquired from the inspections were processed by using a platform which estimated the risks connected to the infrastructure, including the predictive mode. The algorithms, namely Fast Fourier Transform (FFT) and Artificial Intelligence (AI), i.e. Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), were applied to monitor ballast fouling and to predict dangerous operating conditions as in the case of a train which collides into a tunnel, railway track deformation, and other potential structural failures. The work was carried out within the framework of a research industrial project, which aimed at the development of an informatic platform for the geolocation of the risk maps.
Year
DOI
Venue
2021
10.1109/MetroInd4.0IoT51437.2021.9488467
2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)
Keywords
DocType
ISBN
Long Short-Term Memory,GPR,Laser Scanner,Temperature Monitoring,Fast Fourier Transform,Railway Risk Modelling
Conference
978-1-6654-2994-8
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Alessandro Massaro102.37
Giovanni Dipierro202.03
Sergio Selicato311.71
Emanuele Cannella401.69
Angelo Galiano501.69
Annamaria Saponaro601.01