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Advantages of using statistical models for detecting faulty components in railway bogies against using simple criteria as defined in standards

In general, railway vehicles are maintained preventively within certain time periods. Condition-based predictive maintenance strategies offer great economic potential because components can be used much longer and are not exchanged when they are still healthy. Some modern trains are equipped with many sensors on the bogie in order to perform diagnostics and prognostics of components. Within a railway bogie the yaw damper plays an important role because it helps to ensure a stable run of the bogie. Standards define an instability criterion for running safety based on domain knowledge. We analyse the sensitivity of the criterion on a faulty yaw damper. On the other hand, data-driven algorithms gain significance in diagnostics, which as the saying goes can obtain a much better performance. In this work, we compare the instability criterion against the prediction of a statistical model trained on real measured data. We investigate the detection of a yaw damper with reduced functionality. We also analyse the influence of operational conditions such as equivalent conicity and speed. Results show that data-driven classifiers are much more sensitive for faulty states and the prediction of a reduced functionality is possible. Even a faulty yaw damper with low equivalent conicity can be detected.