3D Railway Modelling for Extending the Remaining Useful Life of a Bogie

Railway bogies are typically engineered with conservative safety margins, which frequently results in the premature disposal of components retaining significant structural integrity. This study proposes a comprehensive 3D modelling framework designed to accurately predict and extend the Remaining Useful Life (RUL) of the bogie structure. To achieve this, a Building Information Modelling (BIM) approach was used, not only for the bogie, but for all train, using a rolling stock in Portugal as a case study. The use of both real and virtual sensors installed in the bogie, with data collected with a sampling rate according to the specificity of each sensor and, next, managed through machine learning tools, allows to implement a predictive maintenance (PdM) policy that aid to extend the RUL. The proposed approach demonstrates that extending the operational life of the bogie is both feasible and safe. This facilitates a strategic transition from the current practices to new approaches that improve the Availability of the Physical Assets, including through the metaverse.