Experimental study demonstrates wheel-flat identification via axle-box vibration signals in freight trains, highlighting condition monitoring potential.
Detecting wheelset defects early is crucial for maintaining railway safety.Monitoring the condition of wheelsets provides ongoing insights into the system's health, thereby averting the need for time-consuming and costly periodic inspections.This study focuses on identifying wheel-flat defects in railway wheelsets using vibration signals obtained from axle-box measurements.Experimental campaigns were conducted on a wheelset test bench with defects artificially created.These tests aimed to carry out a time domain analysis on the vibration signals and detect features that can highlight the presence and the severity of a wheelset wheel-flat.Subsequently, an experimental campaign through the employment of sensor nodes was carried out on a Mercitalia freight train (Car T3000) to validate the obtained results.
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Cavallo et al. (2024) studied this question.
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