Evaluation of the effect of speed and defect size on high-frequency acoustic emission and vibration condition monitoring of railway axle bearings

The rail industry has focused on the improvement of maintenance strategies through effective online condition monitoring of critical rolling stock components. The aim is to increase the reliability and minimise the probability of failures. Wheelset defects can develop in-service and evolve rapidly. For this reason, the rail industry has invested tremendously in the development of online wayside wheelset monitoring techniques to minimise the likelihood of a catastrophic derailment caused by wheel and axle bearing defects. This paper discusses the results obtained from condition monitoring tests carried out under laboratory conditions and field trials on actual rolling stock with healthy and faulty axle bearings using acoustic emission (AE) and vibration analysis techniques. Various Key Parameter Indicators (KPIs) are used as a means of verifying the presence of bearing defects and evaluation of their severity together with the effect of speed on the AE and vibration measurements.