A Sampling Approach to Extreme Value Distribution for Time-Dependent Reliability Analysis
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. Copyright © 2013 by ASME.
Z. Hu and X. Du, "A Sampling Approach to Extreme Value Distribution for Time-Dependent Reliability Analysis," Journal of Mechanical Design, Transactions of the ASME, American Society of Mechanical Engineers (ASME), Jan 2013.
The definitive version is available at https://doi.org/10.1115/1.4023925
Mechanical and Aerospace Engineering
Article - Journal
© 2013 American Society of Mechanical Engineers (ASME), All rights reserved.