The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Sebastian J. Vollmer - One of the best experts on this subject based on the ideXlab platform.
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multilevel monte carlo for Reliability Theory
Reliability Engineering & System Safety, 2017Co-Authors: Louis J. M. Aslett, Tigran Nagapetyan, Sebastian J. VollmerAbstract:As the size of engineered systems grows, problems in Reliability Theory can become computationally challenging, often due to the combinatorial growth in the number of cut sets. In this paper we demonstrate how Multilevel Monte Carlo (MLMC) — a simulation approach which is typically used for stochastic differential equation models — can be applied in Reliability problems by carefully controlling the bias-variance tradeoff in approximating large system behaviour. In this first exposition of MLMC methods in Reliability problems we address the canonical problem of estimating the expectation of a functional of system lifetime for non-repairable and repairable components, demonstrating the computational advantages compared to classical Monte Carlo methods. The difference in computational complexity can be orders of magnitude for very large or complicated system structures, or where the desired precision is lower.
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Multilevel Monte Carlo for Reliability Theory
arXiv: Computation, 2016Co-Authors: Louis J. M. Aslett, Tigran Nagapetyan, Sebastian J. VollmerAbstract:As the size of engineered systems grows, problems in Reliability Theory can become computationally challenging, often due to the combinatorial growth in the cut sets. In this paper we demonstrate how Multilevel Monte Carlo (MLMC) - a simulation approach which is typically used for stochastic differential equation models - can be applied in Reliability problems by carefully controlling the bias-variance tradeoff in approximating large system behaviour. In this first exposition of MLMC methods in Reliability problems we address the canonical problem of estimating the expectation of a functional of system lifetime and show the computational advantages compared to classical Monte Carlo methods. The difference in computational complexity can be orders of magnitude for very large or complicated system structures.
Kai-yuan Cai - One of the best experts on this subject based on the ideXlab platform.
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A Health Monitoring Method for Li-ion Batteries Based on Profust Reliability Theory
Chemical engineering transactions, 2013Co-Authors: Zhiyao Zhao, Quan Quan, Kai-yuan CaiAbstract:This paper presents an applicable health monitoring method for Li-ion batteries based on profust Reliability Theory. The word “profust” derives from “probability” and “fuzzy-state”, which means that the profust Reliability Theory is a kind of fuzzy Reliability Theory based on probability assumption and fuzzy-state assumption. It defines a concept of transition from fuzzy success to fuzzy failure to characterize system performance. In this paper, charge cycles of a battery are analysed, and the profust Reliability is employed to estimate battery health state as a health indicator. Then, all operational states are classified into five health levels through profust Reliability values. This process contributes a transform from quantitative Reliability index to qualitative health level, which is able to describe system performance in a more intuitive way. Owing to the appealing ‘fuzzy’ advantage, the profust Reliability Theory can be effectively applied to the health monitoring of Li-ion batteries.
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Posbist Reliability Theory in Terms of System States
The Kluwer International Series in Engineering and Computer Science, 1996Co-Authors: Kai-yuan CaiAbstract:In this chapter we still focus on posbist Reliability Theory, i.e., the Theory of Reliability based on the possibility assumption and the binary-state assumption [2].
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Profust Reliability Theory
The Kluwer International Series in Engineering and Computer Science, 1996Co-Authors: Kai-yuan CaiAbstract:Profust Reliability Theory is based on the probability assumption and the fuzzy-state assumption: A1. Probability assumption: the system failure behavior is fully characterized in the context of probability measures. A2. Fuzzy-state assumption: the system success and failure are characterized by fuzzy states. At any time the system can be viewed as being in one of the two fuzzy states to some extent. That is, the meaning of system failure is not defined in a precise way, but in a fuzzy way.
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Posbist Reliability Theory in Terms of System Lifetimes
The Kluwer International Series in Engineering and Computer Science, 1996Co-Authors: Kai-yuan CaiAbstract:Posbist Reliability Theory is based on the possibility assumption and the binary-state assumption [2]: A1’. Possibility assumption: the system failure behavior is fully characterized in the context of possibility measures. A2’. Binary-state assumption: the system demonstrates only two crisp states: fully functioning or fully failed. At any time the system is in one of the two states.
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Coherent Systems in Profust Reliability Theory
Reliability and Safety Analyses under Fuzziness, 1995Co-Authors: Kai-yuan Cai, Chuan-yuan Wen, Ming-lian ZhangAbstract:Profust Reliability Theory is based on the probability assumption and the fuzzy-state assumption. In an attempt to provide a uniform foundation for profust Reliability Theory, in this paper we introduce the concept of coherent systems and distinguish two classes of systems: closely coherent systems and loosely coherent systems. To formulate the relationships between system reliabilities and component reliabilities, we present a number of general results, results concerning convexity, and results concerning unimodality. It is argued that for a coherent system, component Reliability improvements don’t certainly enhance the system Reliability.
Gert De Cooman - One of the best experts on this subject based on the ideXlab platform.
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On modeling possibilistic uncertainty in two-state Reliability Theory
Fuzzy Sets and Systems, 1996Co-Authors: Gert De CoomanAbstract:In this paper, we show how a possibilistic uncertainty model can be used to represent and manipulate uncertainty about the states of a system and of its components. At the same time, we present a thorough study of the incorporation of this possibilistic uncertainty model in classical, two-state Reliability Theory. The possibilistic Reliability of a component or system is introduced and studied. Furthermore, we introduce the important notion of a possibilistic structure function, based upon the concept of a classical, two-valued structure function. Under certain conditions of possibilistic independence, it allows us to calculate the possibilistic Reliability of a system in terms of the possibilistic reliabilities of its components. Finally, we give straightforward methods for determining a possibilistic structure function from its classical, two-valued counterpart. In this way, we intend to show that a possibilistic uncertainty model in two-state Reliability Theory is formally analogous to, and certainly not more complicated than, a probabilistic uncertainty model.
Louis J. M. Aslett - One of the best experts on this subject based on the ideXlab platform.
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multilevel monte carlo for Reliability Theory
Reliability Engineering & System Safety, 2017Co-Authors: Louis J. M. Aslett, Tigran Nagapetyan, Sebastian J. VollmerAbstract:As the size of engineered systems grows, problems in Reliability Theory can become computationally challenging, often due to the combinatorial growth in the number of cut sets. In this paper we demonstrate how Multilevel Monte Carlo (MLMC) — a simulation approach which is typically used for stochastic differential equation models — can be applied in Reliability problems by carefully controlling the bias-variance tradeoff in approximating large system behaviour. In this first exposition of MLMC methods in Reliability problems we address the canonical problem of estimating the expectation of a functional of system lifetime for non-repairable and repairable components, demonstrating the computational advantages compared to classical Monte Carlo methods. The difference in computational complexity can be orders of magnitude for very large or complicated system structures, or where the desired precision is lower.
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Multilevel Monte Carlo for Reliability Theory
arXiv: Computation, 2016Co-Authors: Louis J. M. Aslett, Tigran Nagapetyan, Sebastian J. VollmerAbstract:As the size of engineered systems grows, problems in Reliability Theory can become computationally challenging, often due to the combinatorial growth in the cut sets. In this paper we demonstrate how Multilevel Monte Carlo (MLMC) - a simulation approach which is typically used for stochastic differential equation models - can be applied in Reliability problems by carefully controlling the bias-variance tradeoff in approximating large system behaviour. In this first exposition of MLMC methods in Reliability problems we address the canonical problem of estimating the expectation of a functional of system lifetime and show the computational advantages compared to classical Monte Carlo methods. The difference in computational complexity can be orders of magnitude for very large or complicated system structures.
Y Gao - One of the best experts on this subject based on the ideXlab platform.
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The possibilistic Reliability Theory: theoretical aspects and applications
Structural Safety, 1997Co-Authors: Christian Cremona, Y GaoAbstract:Abstract The need to treat uncertainties in the design or assessment of structures today induces a lot of concerns in order to ensure the best security levels. This paper presents an original alternative to the probabilistic Reliability Theory, keeping the same features regarding some theoretical concepts (design points, Reliability indexes) but highlighting easy implementations. The probabilistic or classical Reliability Theory is built on the principles of probability Theory which introduces the probabilistic measure for evaluating confidence levels; the Theory introduced in this paper is based on the possibility Theory which uses a new confidence measure: the measure of possibility. While the probabilistic approach requires optimization of a multivariate function, the possibilistic approach reduces the study to the optimization of a variable function. Care is taken in explaining how to implement such a technique and for estimating distributions of possibility. Comparisons between the possibilistic and the probabilistic Reliability theories are also given. Finally a section is dedicated to a concrete application: the Reliability assessment of welded joints damaged by fatigue. This example provides a full application of the possibilistic Reliability Theory, from uncertainty modeling and possibility distribution estimation to failure possibility determinations.
