UC San Diego has developed a methodology for predicting and updating the Remaining Fatigue Life (RFL) of monitored aerospace structures and/or structural sub-components. According to this framework, NDE inspection results and Bayesian inference are used to (a) assess the current state of damage of the system and (b) update the probability distribution functions of the damage extents and damage evolution model parameters. Probabilistic models for future operational loads and calibrated mechanics- based damage evolution models are then used as essential predictive tools to propagate stochastically the identi ed damage mechanisms throughout the pre-identi ed damageable sub-components. Combined local and global failure criteria are used to estimate the time-varying probability of failure and the RFL of the entire structural system. The proposed methodology can lead to either an extent of the RFL - with consequent economical bene ts - or an increase of safety by detecting a fault earlier than anticipated.