  Physics-based and data-driven models for remaining useful lifetime (RUL)
prediction typically suffer from two major challenges that limit their
applicability to complex real-world domains: (1) incompleteness of
physics-based models and (2) limited representativeness of the training dataset
for data-driven models. Combining the advantages of these two directions while
overcoming some of their limitations, we propose a novel hybrid framework for
fusing the information from physics-based performance models with deep learning
algorithms for prognostics of complex safety-critical systems under real-world
scenarios. In the proposed framework, we use physics-based performance models
to infer unobservable model parameters related to a system's components health
solving a calibration problem. These parameters are subsequently combined with
sensor readings and used as input to a deep neural network to generate a
data-driven prognostics model with physics-augmented features. The performance
of the hybrid framework is evaluated on an extensive case study comprising
run-to-failure degradation trajectories from a fleet of nine turbofan engines
under real flight conditions. The experimental results show that the hybrid
framework outperforms purely data-driven approaches by extending the prediction
horizon by nearly 127\%. Furthermore, it requires less training data and is
less sensitive to the limited representativeness of the dataset compared to
purely data-driven approaches.
