Improved Railway Track Irregularities Classification by a Model Inversion Approach
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.
Sensorgestützte Anomaliedetektion zur Zustandsbewertung der Schiene mit Regelzügen
Die Erfassung und automatische Bewertung von georeferenzierten, im regularen Betrieb fahrzeugseitig 
gemessenen Achslagerbeschleunigungen ermoglicht die fruhzeitige Erkennung von Gleisauffalligkeiten. Am Deutschen Zentrum fur Luft- und Raumfahrt e. V. (DLR) wird dazu an der gleisgenauen Positionierung von Schienenfahrzeugen sowie der Bearbeitung und Analyse von Beschleunigungsdaten 
geforscht.
Track state control with use of real time digital data processing
To realise optimal riding comfort on high speed trains running on Shinkansen, it is necessary to remove the long wave components of track irregularity that correspond to the natural frequency of vehicles. To execute the maintenance work on such components effectively, it is necessary to pick up problem spots completely. From this point of view, two methods for getting track waveform on the ground are given. First is the inertia method. In this method, the distorted waveform is rectified in real-time. Second is the inverse filtering method, which restores the track waveform on the ground from l0m-versine. The restoration is realised accurately in real-time. Then, the track state control considering both vehicle dynamics and human sensibility is given. The latter will be an effective and economical way of track maintenance planning.