Online Track Anomaly Detection: Comparison of Different Machine Learning Techniques Through Injection of Synthetic Defects on Experimental Datasets

The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific track defects, allowing significant improvements in terms o...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Giovanni Bellacci, Luca Di Carlo, Marco Fiaschi, Luca Bocciolini, Carmine Zappacosta, Luca Pugi
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2026-04-01
Schriftenreihe:	Machines
Schlagworte:	anomaly detection on railway tracks machine learning measurement of contact forces one-class support vector machine isolation forest local outlier factor
Online-Zugang:	https://www.mdpi.com/2075-1702/14/4/424
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