The Equivalent Conicity is an indirect measure that is indicative of the amount of dynamic instability for a given wheelset. Therefore, it cannot be acquired directly from a physical component, but needs to be determined with related physical measures. The interaction between the conical wheel profiles, their diameter, the track profile, and its gauge, produces bogie hunting oscillations that may incur the risk of derailment if they are allowed to reach large magnitude values, which are attained as the wheel treads degrade with use. This is especially critical for high-speed rail environments. However, the Equivalent Conicity figure helps to quantify this effect and thus drive an effective wheel reprofiling maintenance schedule. This article conducts an evaluation of the Equivalent Conicity algorithm for two trains of the British Rail Class 390 fleet (Pendolino). It assesses the standard calculation approach based on the differential equations, and develops a data-driven approach based on a deep ensemble of features with neural networks. The corresponding values provided by Delta Rail are used as the ground truth. The results of the analysis prove that this method meets the requirements of the maintenance staff and thus yields a new avenue for business improvement through the application of the condition maintenance approach for wheelsets.
How to Cite
railway, wheelset, equivalent conicity, deep, ensemble, neural network
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