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Random Fuzzy Extension of the Universal Generating Function Approach for the Reliability Assessment of Multi-State Systems Under Aleatory and Epistemic Uncertainties | IEEE Journals & Magazine | IEEE Xplore

Random Fuzzy Extension of the Universal Generating Function Approach for the Reliability Assessment of Multi-State Systems Under Aleatory and Epistemic Uncertainties


Abstract:

Many engineering systems can perform their intended tasks with various levels of performance, which are modeled as multi-state systems (MSS) for system availability and r...Show More

Abstract:

Many engineering systems can perform their intended tasks with various levels of performance, which are modeled as multi-state systems (MSS) for system availability and reliability assessment problems. Uncertainty is an unavoidable factor in MSS modeling, and it must be effectively handled. In this work, we extend the traditional universal generating function (UGF) approach for multi-state system (MSS) availability and reliability assessment to account for both aleatory and epistemic uncertainties. First, a theoretical extension, named hybrid UGF (HUGF), is made to introduce the use of random fuzzy variables (RFVs) in the approach. Second, the composition operator of HUGF is defined by considering simultaneously the probabilistic convolution and the fuzzy extension principle. Finally, an efficient algorithm is designed to extract probability boxes (p -boxes) from the system HUGF, which allow quantifying different levels of imprecision in system availability and reliability estimation. The HUGF approach is demonstrated with a numerical example, and applied to study a distributed generation system, with a comparison to the widely used Monte Carlo simulation method.
Published in: IEEE Transactions on Reliability ( Volume: 63, Issue: 1, March 2014)
Page(s): 13 - 25
Date of Publication: 20 January 2014

ISSN Information:


I. Introduction

Multi-State System (MSS) modeling has been widely applied to resolve system availability and reliability assessment problems [1], [2]. Under this framework, the performance of each component is discretized into more than two exclusive states from perfectly functioning to complete failure, and each state is characterized by a probability of occurrence. In general, the intermediate state can be decided through the level of degradation of the component or system function requirements or both. Many components are subject to natural deteriorations which can render them partially functioning, and the system function requirement might force one component to reduce its performance level, even if it bears no degradation. Compared to binary-state system (BSS) models, the MSS models offer higher flexibility in the description of the system state distribution and evolution, for more precise approximations of real-world systems. MSS is a modeling framework capable of handling both availability and reliability assessments. In this paper, we focus on availability assessment assuming the system is repairable.

References

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