Stochastic Characterization and Update of Fatigue Loading for Mechanical Damage Prognosis

You Ling, Christopher Shantz, Shankar Sankararaman, and Sankaran Mahadevan
Submission Type: 
Full Paper
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phmc_10_079.pdf691.74 KBAugust 30, 2010 - 4:40pm

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.

Publication Control Number: 
079
Submission Keywords: 
fatigue loading; rainflow counting; Markov chain; ARMA; uncertainty quantification; Bayesian updating; model validation; prognosis
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