Analysis of Acoustic Emission Waveforms by Wavelet Packet Transform for the Detection of Crack Initiation Due to Fretting Fatigue in Solid Railway Axles

Railway axles are among the most safety-critical components in rolling stock, as their failure can lead to catastrophic consequences. One of the most subtle damage mechanisms affecting these components is fretting fatigue, which is a particularly challenging damage mechanism in these components, as...

Descripción completa

Detalles Bibliográficos
Autores: Zamorano, Marta, Gómez, María Jesús, Castejon, Cristina, Carboni, Michele
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Málaga
Repositorio:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
Idioma:inglés
OAI Identifier:oai:ddfv.ufv.es:10641/6770
Acceso en línea:https://hdl.handle.net/10641/6770
Access Level:acceso abierto
Palabra clave:acoustic emission
ensemble bagged trees
fretting fatigue
solid railway axle
wavelet packet transform
General Materials Science
Instrumentation
General Engineering
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes
Yes
yes
Descripción
Sumario:Railway axles are among the most safety-critical components in rolling stock, as their failure can lead to catastrophic consequences. One of the most subtle damage mechanisms affecting these components is fretting fatigue, which is a particularly challenging damage mechanism in these components, as it can initiate cracks under real service conditions and is difficult to detect in its early stages, which is vital to ensure operational safety and to optimize maintenance strategies. This paper focuses on the development of fretting fatigue damage in solid railway axles under realistic service-like conditions. Full-scale axle specimens with artificially induced notches were subjected to loading conditions that promote fretting fatigue crack initiation and growth. Acoustic emission techniques were used to monitor the damage progression, and post-processing of the emitted signals, by using wavelet-based tools, was conducted to identify early indicators of crack formation. The experimental findings demonstrate that the proposed approach allows for reliable identification of fretting-induced crack initiation, contributing valuable insights into the in-service behavior of railway axles under this damage mechanism.