Stochastic prediction of fatigue loading using real-time monitoring data

Accurate characterization and prediction of loading, while properly accounting for uncertainty, are essential for probabilistic fatigue damage prognosis. Three different techniques – rainflow counting, the Markov chain method, and autoregressive moving average (ARMA) modeling – are investigated for stochastic characterization and reconstruction of the fatigue load history. The ARMA method is extended in this paper by introducing random coefficients and probabilistic weights, to account for the uncertainty in the selection of the model, inherent variability in loading, and uncertainty due to sparse data. A continuous model updating approach based on real-time monitoring data is developed and applied to all the three techniques mentioned above. The relation between prediction accuracy and updating interval is evaluated quantitatively. A quantitative model validation approach using Bayesian hypothesis testing is proposed to assess the confidence in load prediction from all the three methods.