Early identification of railway rolling stock wheel and axle bearing defects using acoustic emission techniques
P Krusuansombat1, P Vallely1, 2, S Kaenwunruen3 and M Papaelias1
1School of Metallurgy and Materials, The University of Birmingham, Birmingham, UK
Email: pxk757@student.bham.ac.uk
2Network Rail, Baskerville House, Birmingham, UK
3School of Engineering, The University of Birmingham, Birmingham, UK
Rolling stock wheels and axle bearings operate under challenging in-service conditions. As a result, various defects can develop with time and if remain undetected propagate rapidly to failure. Catastrophic failure of a railway wheelset will lead to derailment. Derailments can cause major network disruption and can result in casualties, as well as potentially damage the local environment. The non-destructive evaluation of wheels and axle bearings has been traditionally based on inspections taking place during maintenance and using online systems such as hot axle box detectors together with wheel impact and profile detectors. Although wheel impact load and profile detectors provide reliable measurements, they are expensive devices and do not detect axle bearing faults. On the other hand, hot axle box detectors, apart from being expensive, can only detect faulty axle bearings only after they have overheated, which is effectively after they have failed. In this paper, a novel low-cost, non-intrusive plug-and-play system based on acoustic emission is presented together with results from field measurements carried out on the UK railway network in Cropredy.
Keywords: railway, axle bearing, wheel, remote condition monitoring, acoustic emission.
Keywords: railway, axle bearing, wheel, remote condition monitoring, acoustic emission.
