Abstract
Acoustic emission (AE) can be employed for the early fault detection of rolling stock wheelset components. Research has been carried out on the development of a remote condition monitoring (RCM) technology for monitoring online rolling stock wheelset defects. Railway axle bearings and wheels are critical components that can develop faults at any time when in service. AE is a reliable passive RCM technique that can be employed for the quantitative evaluation of the structural integrity of rolling stock wheelsets. The emphasis of this study is placed on the results obtained from experimental work performed under laboratory and field testing conditions. Several laboratory tests were carried out using different axle bearing defects. In addition, a customised online RCM system installed on the Chiltern Rail Line, adjacent to a hot box axle detector, was used for comparison purposes. Using this, AE signal analysis was carried out in order to detect potential rolling stock faults. Defect type evaluation and quantification can also be achieved, leading to effective diagnosis of the structural rolling stock integrity of rolling stock wheels and axle bearings.
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