Abstract
Prognostics methods predict the remaining useful life (RUL) of systems. Data-driven methods generally result in a rigid RUL estimation model that lacks flexibility in adapting to user requirements. In this work, we reformulate the data-driven learning problem inspired by physics-based solutions. Our novel prediction architecture allows us to adjust the level of conservatism in RUL predictions without retraining the data-driven models when the performance thresholds are modified. We demonstrate the validity of our approach on a full-wave rectifier with multiple degrading components and different performance specifications.
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