Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-01, Vol.23 (3), p.1544
Main Authors: Silva, R, Guedes, A, Ribeiro, D, Vale, C, Meixedo, A, Mosleh, A, Montenegro, P
Format: Article
Language:English
Subjects:
Accelerometers
Accident investigations
Artificial intelligence
Cluster analysis
Damage prevention
Data compression
Data integration
Derailments
Design
Dynamic response
Freight traffic
freight traffic loads
Freight trains
Load
Loads (forces)
Methodology
Methods
Monitoring
Monitoring systems
Numerical analysis
Outliers (statistics)
Railroad accidents & safety
Railroads
Sensors
Simulation methods
Strain gauges
Traffic
train–track interaction
unbalanced vertical loads
Vehicles
Vertical loads
Wavelet transforms
wayside condition monitoring
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Summary:The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train-track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23031544