Smart railways: AI-based track-side monitoring for wheel flat identification

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Mohammadi, M (Author) Other; Mosleh, A (Author) FEUP; Cecília Vale (Author) FEUP; Ribeiro, D (Author) Other; Pedro Aires Montenegro (Author) FEUP; Meixedo, A (Author) Other

Journal

Title: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit

Vol. 239

Pages: 272-289

ISSN: 0954-4097

Publisher: SAGE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-017-YY6

DOI: 10.1177/09544097251313570

Abstract (EN): The 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 algorithm to identify and localize railway wheel flats, which considers three stages: (i) wheel flat detection to distinguish a healthy wheel from a damaged one using outlier analysis, achieving 100 percent accuracy; (ii) localizing the damage to pinpoint the location of the defective wheel through the Hidden Markov Model (HMM); (iii) classification of wheel damage based on its severity using k-means clustering technique. The unsupervised learning algorithm is validated with artificial data attained from a virtual wayside monitoring system related to freight train passages with healthy wheels and defective wheels with single and multiple defects. The proposed methodology demonstrated efficiency and robustness for wheel flat detection, localization, and damage severity classification regardless of the number of defective wheels and their position.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18