Damage Phenomena Characterization In Rcf Tests Using Image Analysis And Vibration-Based Machine Learning

Rolling Contact Fatigue (RCF) tests are a common effective method to study the behavior of wheel- and rail-steels. The measurements usually performed are discrete and destructive: they can only be performed at each intermediate stop of a test and they result in the alteration or destruction of the examined specimens. This work aims to assess the damage level steel samples during RCF tests, making continuous, non-destructive, and contactless measurements. A machine-learning technique based on vibration and torque measurements, together with 2D image was applied to RCF-dry tests carried out on different railway wheel steels tested according to the same operating parameters.The proposed algorithm was able to quantitatively estimate the damage level of the samples by calculating the current data distance from specific references, e.g. a defined final damaged state. The used approach ensures a good degree of reliability both in terms of specificity and sensitivity