4 documents found, page 1 of 1
Smart railways: AI-based track-side monitoring for wheel flat identification
Mohammadi, M; Mosleh, A; Cecília Vale; Ribeiro, D; Pedro Aires Montenegro; Meixedo, AThe wheel flat detection in trains using Artificial Intelligence (AI) has emerged as a critical advancement in railway maintenance and safety practices. AI systems can effectively identify geometric deformation in wheel rotation patterns, indicative of potential wheel flat damage, resorting to wayside monitoring systems and machine learning algorithms. This study aims to propose an unsupervised learning algorit...
Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicl...
Guedes, A; Silva, R; Ribeiro, D; Magalhaes, J; Jorge, T; Cecília Vale; Meixedo, A; Mosleh, A; Pedro Aires MontenegroPolygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical...
Early identification of out-of-roundness damage wheels in railway freight vehic...
Jorge, T; Magalhaes, J; Silva, R; Guedes, A; Ribeiro, D; Cecília Vale; Meixedo, A; Mosleh, A; Pedro Aires Montenegro; Cury, AEarly identification of wheel defects can prevent serious damage to railways, considerably lowering maintenance costs for both railway administrations and rolling stock operators. Within this context, an unsupervised methodology based on artificial intelligence techniques is presented, which allows the detection and classification of out-of-roundness damage wheels, such as wheel flats and polygonal wheels, base...
A strategy for out-of-roundness damage wheels identification in railway vehicle...
Magalhaes, J; Jorge, T; Silva, R; Guedes, A; Ribeiro, D; Meixedo, A; Mosleh, A; Cecília Vale; Montenegro, P; Cury, AWayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness (OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system an...