In this study the opportunity to introduce PHM (prognostic and health monitoring) concepts into a cracked railway axle management is investigated.
The performances of two different prognostic algorithm are assessed on the basis of their RUL (remaining useful life) predictions accuracy: a prognostic model based on the Bayesian theory and a physical prognostic model. Both models rely on the measured crack size. The measured crack growth measure has been built from simulated probabilistic crack growth path by adding measurements errors. The effect of monitoring frequency and the measurement HW and SW infrastructure size error to RUL predictions’ accuracy is assessed as well, trying to evaluate the hypothetical measuring infrastructure capabilities’ (sensors + layout) effect on the overall PHM predictions. Furthermore the PHM approach is compared to the classical preventive maintenance approach to railway axle maintenance management based on expensive and regular NDT.
How to Cite
condition based maintenance (CBM), POD, Railways axles, Crack propagation
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