Accession Number : ADA589470


Title :   Time-dependent Reliability of Dynamic Systems using Subset Simulation with Splitting over a Series of Correlated Time Intervals


Descriptive Note : Conference paper


Corporate Author : ARMY TANK AUTOMOTIVE RESEARCH DEVELOPMENT AND ENGINEERING CENTER WARREN MI


Personal Author(s) : Wang, Zhonglai ; Mourelatos, Zissimos P ; Li, Jing ; Singh, Amandeep ; Baseski, Igor


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a589470.pdf


Report Date : Aug 2013


Pagination or Media Count : 19


Abstract : Time-dependent reliability is the probability that a system will perform its intended function successfully for a specified time. Unless many and often unrealistic assumptions are made, the accuracy and efficiency of time-dependent reliability estimation are major issues which may limit its practicality. Monte Carlo simulation (MCS) is accurate and easy to use but it is computationally prohibitive for high dimensional, long duration, time-dependent (dynamic) systems with a low failure probability. This work addresses systems with random parameters excited by stochastic processes. Their response is calculated by time integrating a set of differential equations at discrete times. The limit state functions are therefore, explicit in time and depend on time-invariant random variables and time-dependent stochastic processes. We present an improved subset simulation with splitting approach by partitioning the original high dimensional random process into a series of correlated, short duration, low dimensional random processes. Subset simulation reduces the computational cost by introducing appropriate intermediate failure sub-domains to express the low failure probability as a product of larger conditional failure probabilities. Splitting is an efficient sampling method to estimate the conditional probabilities. The proposed subset simulation with splitting not only estimates the timedependent probability of failure at a given time but also estimates the cumulative distribution function up to that time with approximately the same cost. A vibration example involving a vehicle on a stochastic road demonstrates the advantages of the proposed approach.


Descriptors :   *RELIABILITY , *SIMULATION , *TIME DEPENDENCE , *TIME INTERVALS , MONTE CARLO METHOD , SPLITTING , STOCHASTIC PROCESSES


Subject Categories : Operations Research
      Computer Programming and Software
      Test Facilities, Equipment and Methods
      Electricity and Magnetism


Distribution Statement : APPROVED FOR PUBLIC RELEASE