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Vehicle System Dynamics
International Journal of Vehicle Mechanics and Mobility
Volume 60, 2022 - Issue 6
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Articles

Rail-wheel contact forces and track irregularity estimation from on-board accelerometer data

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Pages 2145-2166 | Received 27 Oct 2020, Accepted 18 Feb 2021, Published online: 15 Mar 2021
 

Abstract

Rolling stock approval protocols, such as UIC 518, demand monitoring of lateral and vertical forces at rail-wheel contact. Continuous monitoring through on-board instrumentation needs algorithms to convert the on-board sensor data into rail-wheel forces and track irregularities. In this work a Feed Forward Neural Network architecture, for fast and accurate estimation of rail-wheel forces and track irregularities from data obtained from a cluster of accelerometers mounted on the rolling stock, is presented. Simulation study is conducted for a passenger coach running over numerically generated set of one hundred rail tracks, each with a different irregularity. Power spectral density (PSD) function of ERRI B176 is used for creating rail irregularities. Multi-body rail coach model consists of wheel-sets, axle-box, bogie, bolster and car body. The input comprises acceleration, yaw and roll rate data from axle-box, bogie and car body, to produce contact forces and rail irregularities as outputs. Goodness of fit between actual and estimated values is illustrated through point-by-point graphs of actual and estimated track forces and irregularities. R-Squared (R2) values, representing the fraction by which the variance of errors are less than the variance of actual values, are computed as indices of the accuracy of estimates.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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