Rekha Sundararajan
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The paper proposes a multi-task scientific machine learning framework that jointly predicts key engine health indicators (TGTU, DTGT) and the Remaining Useful Life (RUL) while quantifying prediction uncertainty for robust, risk-aware maintenance decisions.
This paper benchmarks five distinct uncertainty quantification methods—including Delta, Bayesian Dropout, and Bootstrap—to determine the optimal approach for predicting turbine gas temperature degradation while maintaining both accuracy and reliable prediction intervals.
Papers
Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction
Jostein Barry-Straume, Changmin Son, Adrian Sandu, Gavan Burke +3 more
The paper proposes a multi-task scientific machine learning framework that jointly predicts key engine health indicators (TGTU, DTGT) and the Remaining Useful Life (RUL) while quantifying prediction u…