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Article Citation - WoS: 6Citation - Scopus: 6Reliability Analysis of Systems With Components Having Two Dependent Subcomponents(Taylor & Francis inc, 2017) Eryilmaz, SerkanIn this article, a system that consists of n independent components each having two dependent subcomponents (A(i), B-i), i = 1, ... ,n is considered. The system is assumed to compose of components that have two correlated subcomponents (A(i), B-i), and functions iff both systems of subcomponents A(1),A(2), ... ,A(n) and B-1, B-2, ... , B-n work under certain structural rules. The expressions for reiiabiiity and mean time to failure of such systems are obtained. A sufficient condition to compare two systems of bivariate components in terms of stochastic ordering is also ordering presented.Book Part Discrete Scan Statistics Generated by Dependent Trials and Their Applications in Reliability(Springer New York, 2024) Eryilmaz,S.; Yalcin,F.The chapter is concerned with discrete scan statistic based on a sequence of dependent binary trials. In particular, the existing results are reviewed for the distribution of the discrete scan statistic based on a sequence of exchangeable binary trials. The results are discussed in the context of the reliability of the linear consecutive-k-within-m-out-of-n:F system, and a new exact formula for the reliability of the linear consecutive-2-within-m-out-of-n:F system that consists of arbitrarily dependent components is presented. © Springer Science+Business Media, LLC, part of Springer Nature 2024.Article Citation - WoS: 19Citation - Scopus: 20(k1< k2< km< System and Its Reliability(Elsevier Science Bv, 2019) Eryilmaz, SerkanThis paper is concerned with a system consisting of multiple types of components and having (k(1), k(2),..., k(m))-out-of-n structure. The (k(1), k(2),.., k(m))-out-of-n system is a system consisting of n components of type i, i = 1, 2,..., m, and functions if at least k(1) components of type 1, k(2) components of type 2,..., k(m) components of type m work, n = Sigma(n)(i=1) n(i). The exact and approximate expressions are obtained for the survival function of the system under concern. The weighted-(k(1), k(2),..., k(m))-out-of-n system is also defined and studied. This weighted model is applied to evaluate the wind power system that consists of two wind plants. (C) 2018 Elsevier B.V. All rights reserved.Article Citation - WoS: 1Citation - Scopus: 2On the First Time of Ruin in Two-Dimensional Discrete Time Risk Model With Dependent Claim Occurrences(Taylor & Francis inc, 2018) Eryilmaz, SerkanThis article is concerned with a two-dimensional discrete time risk model based on exchangeable dependent claim occurrences. In particular, we obtain a recursive expression for the finite time non ruin probability under such a dependence among claim occurrences. For an illustration, we define a bivariate compound beta-binomial risk model and present numerical results on this model by comparing the corresponding results of the bivariate compound binomial risk model.Article Citation - WoS: 4Citation - Scopus: 4On compound sums under dependence(Elsevier, 2017) Eryilmaz, SerkanIn this paper, we study the compound random variable S = Sigma(N)(t-1) Y-t when there is a dependence between a random variable N and a sequence of random variables {Y-t}(t >= 1). Such a compound random variable has been found to be useful in several fields including actuarial science, risk management, and reliability. In particular, we develop some results on distributional properties of the random variable S when N is a phase-type random variable that is defined on a sequence of binary trials and depends on {Y-t}(t >= 1). We "present illustrative examples and an application for the use of results in actuarial science. (C) 2016 Elsevier B.V. All rights reserved.Article Citation - WoS: 7Citation - Scopus: 7A Generalized Class of Correlated Run Shock Models(de Gruyter Poland Sp Zoo, 2018) Yalcin, Femin; Eryilmaz, Serkan; Bozbulut, Ali RizaIn this paper, a generalized class of run shock models associated with a bivariate sequence {(X-i, Y-i)}(i >= 1) of correlated random variables is defined and studied. For a system that is subject to shocks of random magnitudes X-1, X-2, ... over time, let the random variables Y-1, Y-2, ... denote times between arrivals of successive shocks. The lifetime of the system under this class is defined through a compound random variable T = Sigma(N)(t=1) Y-t, where N is a stopping time for the sequence {Xi}(i >= 1) and represents the number of shocks that causes failure of the system. Another random variable of interest is the maximum shock size up to N, i.e. M = max {X-i, 1 <= i <= N}Distributions of T and M are investigated when N has a phase-type distribution.Article Citation - WoS: 4Citation - Scopus: 4On Success Runs in a Sequence of Dependent Trials With a Change Point(Elsevier Science Bv, 2018) Eryilmaz, SerkanLet {X-i}(i=1)(n) be a sequence of n dependent binary trials such that the first n(1) in {X-i}(i=1)(n) are of type 1 and follow an exchangeable joint distribution denoted by L-1, and the last n2 elements in {X-i}(i=1)(n) are of type 2 and follow an exchangeable joint distribution denoted by L-2, where n(1) + n(2) = n. That is, the trials within the same group are exchangeable dependent, and the trials in different groups are dependent in a general sense. The exact distributions of the number of success runs of length k in {X-i}(i=1)(n) are obtained under nonoverlapping and at least schemes. (C) 2017 Elsevier B.V. All rights reserved.Article Citation - WoS: 22Citation - Scopus: 24Reliability Analysis of Weighted-K System Consisting of Three-State Components(Sage Publications Ltd, 2019) Eryilmaz, Serkan; Bozbulut, Ali RizaThe reliability of a weighted-k-out-of-n system that consists of three-state components is studied. The system is assumed to comprise n three-state components, namely, perfect functioning, partial working, and complete failure and functions if the total weight of all the working components is at least k. Reliability expressions are presented when the times spent by components in perfect functioning and partial working states are dependent with a given joint distribution. Sufficient conditions are also provided to compare the expected total weights of two systems.Article Citation - Scopus: 1The Distribution of Wind Power from a Dispersed Array of Wind Turbine Generators and Its Reliability Based Applications(Elsevier, 2026) Eryilmaz, Serkan; Kan, Cihangir; Devrim, YilserIn this paper, the probability distribution of wind power from a dispersed array of wind turbine sites is studied considering forced outage rates of wind turbines. The wind speeds at distinct sites are assumed to be dependent and the dependence is modeled by copulas. In particular, the probability distribution of the aggregate power from two sites is exactly derived. The probability distribution of the aggregate power is also derived under the particular case when site 1 consists of n1 identical wind turbines of type 1 and site 2 consists of n2 identical wind turbines of type 2. Numerical results are presented to illustrate the theoretical findings for a chosen copula function.Article Citation - WoS: 17Citation - Scopus: 25On the Theoretical Distribution of the Wind Farm Power When There Is a Correlation Between Wind Speed and Wind Turbine Availability(Elsevier Sci Ltd, 2020) Kan, Cihangir; Devrim, Yilser; Eryilmaz, SerkanIt is important to elicit information about the potential power output of a wind turbine and a wind farm consisting of specified number of wind turbines before installation of the turbines. Such information can be used to estimate the potential power output of the wind farm which will be built in a specific region. The output power of a wind turbine is affected by two factors: wind speed and turbine availability. As shown in the literature, the correlation between wind speed and wind turbine availability has an impact on the output of a wind farm. Thus, the probability distribution of the power produced by the farm depending on the wind speed distribution and turbine availability can be effectively used for planning and risk management. In this paper, the theoretical distribution of the wind farm power is derived by considering the dependence between turbine availability and the wind speed. The theoretical results are illustrated for real wind turbine reliability and wind speed data.
