LAUSR

Abstract The in-service safety of railway axles is a very important engineering challenge, as it has a large impact not only from the economic point of view of the railway operator, but it has cascading effects on supply chains, loss of work productivity, and, in the most serious cases, loss of life. It is, therefore, vital that the structural integrity of such components is known, during their lifecycle, with the highest possible accuracy via precise modelling, reliable inspections and, more recently but still at research level, effective condition monitoring. With a focus on solid freight axles, the research investigates the applicability of Acoustic Emission as a structural health monitoring approach for determining the in-service condition of a full-scale axle. A fatigue crack propagation test is carried out in the lab subjecting the axle to many repetitions of a block load sequence defined from real service measurements. Acoustic Emission data are continuously recorded during the test, whilst crack size is periodically measured by conventional non-destructive techniques. Eventually, a first-approximation correlation is highlighted between Acoustic Emission data, post-processed by a machine-learning algorithm, and crack propagation ones.