Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison

In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison. It is shown that the proposed maximum relative entropy methodology has narrower confidence bounds of the remaining life prediction than classical Bayesian updating algorithm.