Stochastic Characterization and Update of Fatigue Loading for Mechanical Damage Prognosis



Published Oct 10, 2010
You Ling Christopher Shantz Shankar Sankararaman Sankaran Mahadevan


Accurate characterization and prediction of loading, while properly accounting for uncertainty, are essential for probabilistic fatigue damage prognosis. Three different techniques, including rainflow counting, the Markov chain method, and autoregressive moving average (ARMA) method, are reviewed for stochastic characterization and reconstruction of the fatigue load spectrum for prognosis. The ARMA method is extended by introducing random coefficients and probabilistic weights, to account for the uncertainty in the selection of models, inherent variability of loading, and uncertainty due to sparse data. A continuous model updating framework based on usage monitoring data is developed and applied to all the three techniques mentioned above. The relation between prediction accuracy and updating period is evaluated quantitatively. A quantitative model validation metric is proposed for assessing the accuracy of load prediction

How to Cite

Ling, Y. ., Shantz, C., Sankararaman, . S. ., & Mahadevan, S. . (2010). Stochastic Characterization and Update of Fatigue Loading for Mechanical Damage Prognosis. Annual Conference of the PHM Society, 2(1).
Abstract 174 | PDF Downloads 139



fatigue loading, rainflow counting, Markov chain, ARMA, uncertainty quantification, Bayesian updating, model validation, prognosis

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