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Time-variant reliability prediction for dynamic systems using partial information
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DOI：10.1016/j.ress.2019.106756.png)
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Abstract
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Efficient time-variant reliability prediction for dynamic systems is a challenging problem to reduce the risk because large amounts of information is needed for the prediction. In this paper, a physics-based reliability prediction method is presented with partial information. The cumulative probabilities of failure are first estimated for the given time intervals with complete information based on the subset simulation with splitting and time-variant copula function. An appropriate probability distribution is then selected for fitting the estimated cumulative probabilities of failure. The partial information, which can be collected from mathematical models or the physical experiments during the later time intervals, is used to effectively update the distribution parameters to improve the prediction accuracy. A case study of a vibratory system representing the quarter car model is employed to testify the proposed method.
Keywords:
Time-variant reliability prediction
Subset simulation
Copula function
Physics-based reliability
Dynamic systems
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