Filters

2013 - 20142
Reset filters

Settings

Subject: Monte-Carlo simulation

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 44
    Citation - Scopus: 54
    On Reliability Analysis of a k-out-of-n< System With Components Having Random Weights
    (Elsevier Sci Ltd, 2013) Eryilmaz, Serkan
    Consider a system consisting of n components each with its own positive integer-valued random weight (capacity). The system is assumed to have a performance level above c if there are at least k working components, and the total weight of all working components is above c. We study the reliability properties of such a system. A recursive formula is obtained for computing the system state probabilities. We present a Monte-Carlo simulation algorithm to observe the time spent by the system in state c or above. The algorithm is based on the use of ordered lifetimes of components. We illustrate the results with numerical computations. (C) 2012 Elsevier Ltd. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 37
    Citation - Scopus: 44
    An Algorithmic Approach for the Dynamic Reliability Analysis of Non-Repairable Multi-State Weighted k-out-of-n< System
    (Elsevier Sci Ltd, 2014) Eryilmaz, Serkan; Bozbulut, Ali Riza; Riza Bozbulut, Ali
    In this paper, we study a multi-state weighted k-out-of-n:G system model in a dynamic setup. In particular, we study the random time spent by the system with a minimum performance level of k. Our method is based on ordering the lifetimes of the system's components in different state subsets. Using this ordering along with the Monte-Carlo simulation algorithm, we obtain estimates of the mean and survival function of the time spent by the system in state k or above. We present illustrative computational results when the degradation in the components follows a Markov process. (C) 2014 Elsevier Ltd. All rights reserved.