Experimental Research on Fault Location for the Axle of Railway Vehicles Based on Acoustic Emission Technique

Jiang, Chang; S, Pan; Zhang, Chao; Li, Fu

doi:10.14257/ijca.2016.9.5.09

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Few studies are available in the literature on SHM applied to railway axles and none, for what the authors know, specifically on monitoring fretting fatigue damage. Nevertheless, the more mature proposed approaches for axles seem to be acoustic emission, [25][26][27][28][29] low frequency vibrations (revolution periodicity), 30 high frequency vibrations (shifting of natural frequencies) 31 and automated UT. 32 Among the above-mentioned SHM approaches, this study deals with acoustic emission (AE) 33,34 because, today, it seems to be one of the most mature and because the needed equipment is light, small in size and easy to handle (very important considering the application to axles mounted on running trains).…”

Section: Introductionmentioning

confidence: 99%

On monitoring fretting fatigue damage in solid railway axles by acoustic emission with unsupervised machine learning and comparison to non-destructive testing techniques

Carboni

Zamorano

2023

Proceedings of the Institution of Mechanical Engineers, Part F:

View full text Add to dashboard Cite

Railway axles are safety-critical components of the rolling stock and the consequences of possible in-service failures can have dramatic effects. Although this element is traditionally designed against such failures, the initiation and propagation of service cracks are still occasionally observed, requiring an effective application of non-destructive testing and structural health monitoring approaches. This paper investigates the application of structural health monitoring by acoustic emission to the case of solid railway axles subject to fretting fatigue damage. A full-scale test was performed on a specimen in which artificial notches were suitably manufactured in order to cause the initiation and evolution of fretting fatigue damage up to the stage of relevant propagating fatigue cracks. During the test, both periodical phased array ultrasonic inspections and continuous acquisition of acoustic emission data have been carried out. Moreover, at the end of the test, the specimen was inspected, analyzed and evaluated by visual inspection and magnetic particles testing, while acoustic emission raw data were post-processed by a special unsupervised machine learning algorithm based on an Artificial Neural Network. It is demonstrated that the proposed methodology is very effective to detect the onset of crack initiation in a non-invasive and safe way.

show abstract