Typical railway wheelsets consist of wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and remote condition monitoring of rolling stock to reduce the probability of failure as much as realistically possible. Current wayside systems such as hot axle box detectors and acoustic arrays may fail to detect defective bearings. This article discusses the results of wayside high-frequency acoustic emission measurements performed on freight rolling stock with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis is applied for the analysis of the acoustic emission data. From the results obtained, it is evident that time spectral kurtosis is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel-rail interaction, braking and changes in train speed.

The alternating current field measurement (ACFM) technique can be applied for surface-breaking fatigue crack detection and sizing; the link between the ACFM signal and crack size is well understood for individual cracks. However, the ACFM response to multiple clustered cracks is significantly different to that of isolated cracks. In railway rails the high wheel-rail forces can lead to rolling contact fatigue (RCF) cracks. Often cracks appear together in small clusters or in long stretches. The accurate characterisation of such fatigue cracks is essential for carrying out efficient and safe repair and maintenance. This paper presents a method for sizing the important sub-surface section of multiple cracks using ACFM via the application of an artificial neural network (ANN). The approach is demonstrated using a railway case study: a simulation-based dataset of signal response covering the range of RCF cracks typically seen in in-service railway tracks has been generated to give a thorough representation of the effect of clustered crack parameters on the ACFM response. A 5×5×2×1 multi-layer ANN has been optimised and trained using the validated simulation database to learn the inverse relationship between the crack pocket length (desired output) and the ACFM signal for a given cluster of RCF cracks. The network has been evaluated on a set of experimental data to size cracks of known dimensions from ACFM measurements and also on unseen simulation data. Results from both simulation and experiment show that the approach presented can be used to size clustered cracks to approximately the same degree of accuracy as is possible for isolated cracks.

Alternating current field measurement (ACFM) probes are used to detect and size cracks in a range of engineering components. Crack sizing for this, and other electromagnetic (EM) based NDT systems, relies on relating the signal obtained to the actual crack length. For cracks that do not propagate vertically, such as rolling contact fatigue cracks in rails, predicting the crack depth, which determines the rail depth to be removed by grinding, requires an assumed propagation angle into the material as no method to determine crack vertical angle from the EM signals has been reported. This paper discusses the relationship between ACFM signals and propagation angles for surface-breaking cracks using a COMSOL model. The Bx signal accurately predicts the crack pocket length when the vertical angle is 30-90° but underestimates pocket length for shallower angles, e.g. a 50% underestimate is seen for a 3.2 mm pocket length crack propagating at a vertical angle of 10°. A new measure, the Bz trough-peak ratio, is proposed to determine the crack vertical angle. These are verified by experimental measurements using a commercial ACFM pencil probe for cracks with a range of vertical angles between 10° and 90°.

This paper presents the experimental and model results of the response of an alternating current field measurement (ACFM) sensor to clusters of rolling contact fatigue (RCF) cracks typical of those found in rails and rail wheels. Both artificial and real cracks occurring in rails taken from service are considered. Currently, commercially available ACFM software is capable of producing an estimate of crack pocket length for isolated cracks, assuming they are regularly shaped. The results presented are part of continuing work to link the ACFM signal to the whole range of complex shaped RCF cracks that appear in rail and rail wheels, including those appearing in clusters. The challenges in accurately sizing clustered RCF cracks using the ACFM technique are discussed.

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