  Various engineering systems such as naval and aerial vehicles, offshore
structures, and mechanical components of motorized systems, are exposed to
fatigue failures due to stochastic loadings. Methods for early failure
prediction are essential for engineering, military, and civil applications. In
addition to the prediction of time to failure (TtF), uncertainty quantification
(UQ) is of major importance for real-time decision-making purposes. Usually,
time domain or frequency domain methods are used for fatigue prediction, such
as rainflow counting and Miner's rule or Dirlik's method. However, those
methods suffer from over-simplistic modeling and inaccurate failure predictions
under stochastic loadings. During the last years, several data-driven models
were suggested for offline fatigue failure. However, most of them are not
capable of both accurate real-time fatigue prediction and UQ. In the current
work, a probabilistic data-driven model is introduced. A hybrid architecture of
a fully-connected artificial neural network (FC-ANN) and Gaussian process
regression (GPR) is proposed to ensure enhanced predictive abilities and
simultaneous UQ of the predicted TtF. The real-time prediction and UQ
performances of the suggested model are validated using both synthetic and
experimental data. This novel hybrid method is fully data-driven and extends
the forecasting capabilities of existing time-domain and machine learning-based
methods for fatigue prediction. It paves the way towards the development of a
preventive system that provides real-time safety and operational instructions
and insights for structural health monitoring (SHM) purposes, allowing
prevention of environmental damage, and loss of human lives.
