Due to high-speed of trains, the track deformation increases rapidly and may lead to track irregularities causing the track stability problem. To secure the track stability, the continual inspection on track irregularities is required. The paper presents a methodology for identifying track irregularity using the NARX neural network considering non-linearity in the train structural system. A simulation study has been carried out to examine the proposed method. Acceleration time history data measured at a bogie were re-sampled to every 0.25m track irregularity. In the simulation study, two sets of measured data were simulated. The second data set was obtained by a train with 10% more mass than the one for the first data set. The first set of simulated data was used to train the series-parallel mode of NARX neural network. Then, the track irregularities at the second time period are identified by using the measured acceleration data. The closeness of the identified track irregularity to the actual one is evaluated by PSD and RMSE.
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