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    Assessing railway vehicle derailment potential using neural networks

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    Current methods for ensuring the safe running of railway vehicles assess the track and vehicle condition against fixed limits. Any exceedence of these limits requires remedial action to be taken. The setting of these limits is based on past experience or on computer modelling of vehicle track interaction. This paper describes the initial results of a novel method aimed at predicting vehicle behaviour from track measurements using an artificial neural network. The speed of the neural network method would allow quick analysis of all the data measured by the track recording coach and would also allow maintenance decisions to be based on the effect of track condition on the vehicle behaviour rather than on simple limits
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