  Crack detection, length estimation, and Remaining Useful Life (RUL)
prediction are among the most studied topics in reliability engineering.
Several research efforts have studied physics of failure (PoF) of different
materials, along with data-driven approaches as an alternative to the
traditional PoF studies. To bridge the gap between these two techniques, we
propose a novel hybrid framework for fatigue crack length estimation and
prediction. Physics-based modeling is performed on the fracture mechanics
degradation data by estimating parameters of the Paris Law, including the
associated uncertainties. Crack length estimations are inferred by feeding
manually extracted features from ultrasonic signals to a Neural Network (NN).
The crack length prediction is then performed using the Particle Filter (PF)
approach, which takes the Paris Law as a move function and uses the NN's output
as observation to update the crack growth path. This hybrid framework combines
machine learning, physics-based modeling, and Bayesian updating with promising
results.
