Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction 事件

Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction arXiv:2605.30593v1 Announce Type: cross Abstract: Engine Health Management (EHM) depends on reliable forecasting of Remaining Useful Life (RUL) and on tracking thermal indicators such as turbine gas temperature (TGT). In practice, real-world fleet data are heterogeneous and non-stationary, and point predictions alone are insufficient for risk-aware maintenance decisions. This paper presents a multi-tas