Over time railway networks have become complex Systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures are made up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenance strategy leading to an optimized scheduling of maintenance actions taking the actual aswell as the expected future infrastructure condition into account. A prerequisite is, however, that the thousands of Kilometers of railway tracks are almost continuously monitored. Thus, a promising approach is the usage of low-cost sensors, e.g. accelerometers and gyroscopes, which can be installed on common in-line freight and passenger trains. Due to ambiguous data records a credible classification of railway track irregularities directly from these data is challenging. Alternatively to this pure data-driven approach, in this paper a novel hybrid Approach is presented. To this end, a simplified vehicle Suspension model is applied for the purpose of railway track condition monitoring by analyzing the dynamic railway track - Train interactions. The inversion of the model can be used to recalculate the actual inputs (irregularities) of the monitored system (rail surface) which have caused recorded System Responses (dynamic vehicle reactions and acceleration data, respectively). These recalculated inputs are a sound Basis of subsequent data-driven condition monitoring analyses. In this preliminary study, a classification algorithm is implemented to identify a simulated railway track irregularity automatically
