Maintaining high accuracy and efficiency is a challenging issue in time-dependent reliability analysis. In this work, an accurate and efficient method is proposed for limit-state functions with the following features: The limit-state function is implicit with respect to time. There is only one stochastic process in the input to the limit-sate function. The stochastic process could be either a general strength or a general stress variable so that the limit-state function is monotonic to the stochastic process. The new method employs a sampling approach to estimate the distributions of the extreme value of the stochastic process. The extreme value is then used to replace the corresponding stochastic process. Consequently the time-dependent reliability analysis is converted into its time-invariant counterpart. The commonly used time-invariant reliability method, the first order reliability method, is then applied to calculate the probability of failure over a given period of time. The results show that the proposed method significantly improves the accuracy and efficiency of time-dependent reliability analysis.
A Sampling Approach to Extreme Value Distribution for Time-Dependent Reliability Analysis
Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received December 30, 2012; final manuscript received February 8, 2013; published online May 10, 2013. Assoc. Editor: David Gorsich.
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Hu, Z., and Du, X. (May 10, 2013). "A Sampling Approach to Extreme Value Distribution for Time-Dependent Reliability Analysis." ASME. J. Mech. Des. July 2013; 135(7): 071003. doi: https://doi.org/10.1115/1.4023925
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