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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 () 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.

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