Fuzzy System Reliability Analysis Based on Confidence Interval

2012 ◽  
Vol 433-440 ◽  
pp. 4908-4914 ◽  
Author(s):  
Ezzatallah Baloui Jamkhaneh ◽  
Azam Nozari
Keyword(s):  
Confidence Interval ◽  
System Reliability ◽  
Fuzzy System ◽  
Statistical Data ◽  
Parallel Systems ◽  
Fuzzy Data ◽  
Statistical Methodology ◽  
Fuzzy Reliability ◽  
Numerical Examples ◽  
Cut Set

This paper proposes a new method for analyzing the fuzzy system reliability of a parallel-series and series-parallel systems using fuzzy confidence interval, where the reliability of each component of each system is unknown. To compute system reliability, we are estimated reliability of each component of the systems using fuzzy statistical data with both tools appropriate for modeling fuzzy data and suitable statistical methodology to handle these data. Numerical examples are given to compute fuzzy reliability and its cut set and the calculating was performed by using programming in software R.

scholarly journals System Reliability Analysis Based On Weibull Distribution and Hesitant Fuzzy Set

2018 ◽  
Vol 3 (4) ◽  
pp. 513-521 ◽  
Author(s):  
Akshay Kumar ◽  
Mangey Ram
Keyword(s):  
System Reliability ◽  
Fuzzy Number ◽  
Reliability Function ◽  
Reliability Theory ◽  
Lifetime Distribution ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Reliability ◽  
Averaging Operator ◽  
Cut Set ◽  
Better Than

This work deals with the hesitant fuzzy number and averaging operator and fuzzy reliability with the help of Weibull lifetime distribution. Fuzzy reliability function and triangular hesitant fuzzy number also computed with α-cut set of the proposed reliability function. After applying the averaging operator of hesitant theory, the results are better than simple fuzzy. Also at last, a numerical example has been shown that how the hesitant fuzzy and α-cut work in case of reliability theory.

scholarly journals Parameter Interval Estimation of System Reliability for Repairable Multistate Series-Parallel System with Fuzzy Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wimonmas Bamrungsetthapong ◽  
Adisak Pongpullponsak
Keyword(s):  
Confidence Interval ◽  
System Reliability ◽  
Fuzzy System ◽  
Estimation Method ◽  
Interval Estimation ◽  
Parallel System ◽  
Expected Length ◽  
Parameter Interval ◽  
Sets Theory ◽  
Triangular Membership Function

The purpose of this paper is to create an interval estimation of the fuzzy system reliability for the repairable multistate series–parallel system (RMSS). Two-sided fuzzy confidence interval for the fuzzy system reliability is constructed. The performance of fuzzy confidence interval is considered based on the coverage probability and the expected length. In order to obtain the fuzzy system reliability, the fuzzy sets theory is applied to the system reliability problem when dealing with uncertainties in the RMSS. The fuzzy number with a triangular membership function is used for constructing the fuzzy failure rate and the fuzzy repair rate in the fuzzy reliability for the RMSS. The result shows that the good interval estimator for the fuzzy confidence interval is the obtained coverage probabilities the expected confidence coefficient with the narrowest expected length. The model presented herein is an effective estimation method when the sample size isn≥100. In addition, the optimalα-cut for the narrowest lower expected length and the narrowest upper expected length are considered.

Hybrid Fuzzy Estimation of System Reliability for Multi-State System

2017 ◽  
Vol 866 ◽  
pp. 387-391
Author(s):  
Wimonmas Bamrungsetthapong ◽  
Adisak Pongpullponsak
Keyword(s):  
Confidence Interval ◽  
System Reliability ◽  
Fuzzy System ◽  
Coverage Probability ◽  
Interval Estimation ◽  
Point Estimate ◽  
Hybrid Estimation ◽  
Expected Length ◽  
Fuzzy Parameter ◽  
Fuzzy Parameters

This article is purpose a hybrid estimation of the fuzzy system reliability for the Non-repairable multi-state series-parallel system (NMSS). Considering the fuzzy parameter of NMSS are prior fuzzy parameters. Then the posterior fuzzy parameters of NMSS are constructed by fuzzy Bayesian point estimate of fuzzy system reliability. Moreover, an approach to construct interval estimation of the fuzzy system reliability of NMSS will be used in estimation of the prior fuzzy confidence interval and posterior fuzzy confidence interval of fuzzy system reliability. Finally, the coverage probability and the expected length that it is used to interpret the efficiency of both fuzzy confidence intervals are presented.

Fuzzy System Reliability Using Fuzzy Lifetime Distribution Emphasizing Octagonal Intuitionistic Fuzzy Numbers

2021 ◽  
pp. 192-208
Author(s):  
Pawan Kumar
Keyword(s):  
System Reliability ◽  
Fuzzy System ◽  
Fuzzy Numbers ◽  
Time To Failure ◽  
Fuzzy Reliability ◽  
Mean Time To Failure ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Mean ◽  
Mean Time

The present study proposes to determine the fuzzy reliability of different systems in which the lifetime of components are following fuzzy exponential distribution where fuzzy reliability function and its α-cut set are presented. The fuzzy reliability of different systems is defined on the basis of octagonal intuitionistic fuzzy numbers. The fuzzy reliability functions of k-out-of-m system, series system, parallel systems, and their fuzzy mean time to failure are discussed respectively using the concept of α-cut of octagonal intuitionistic fuzzy numbers. Finally, some numerical examples are discussed to illustrate how to calculate the fuzzy system reliability and α-cut of fuzzy mean time to failure (FMTTF).

Severity of Psoriasis Associated with Metabolic Syndrome

2019 ◽  
Vol 70 (6) ◽  
pp. 2072-2079
Author(s):  
Ana Maria Alexandra Stanescu ◽  
Constantin Stefani ◽  
Ioana Veronica Grajdeanu ◽  
Bogdan Serban ◽  
Gheorghe Ciobanu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Cardiovascular Diseases ◽  
Confidence Interval ◽  
Statistical Data ◽  
Severe Psoriasis ◽  
Negative Consequences ◽  
Chi Square ◽  
Statistical Software ◽  
Total Group ◽  
Chi Square Test

Although extensively studied, psoriasis still has negative consequences and is associated with multiple comorbidities, including metabolic syndrome. The severity of psoriasis seems to influence the occurrence of diagnostic criteria for metabolic syndrome. 208 patients diagnosed with psoriasis were identified, who were divided into lots depending on the severity of psoriasis, but also to the presence or absence of metabolic syndrome. Interpretation of statistical data was done with SPSS V21 (Statistical Package for Social Science) and MEDCALC (Statistical Software). The coexistence of severe psoriasis with metabolic syndrome increases the risk of developing cardiovascular diseases by 2.97 or greater, with a confidence interval of [1.60, 5.51], than that of patients with severe psoriasis who have no metabolic syndrome. The hypothesis was statistically confirmed by p = 0.003. Analyzing the total group with psoriasis by severity, we found the following distribution: from the total number of 208 patients, 39 (18.8%) had severe psoriasis, 83 (39.9%) moderate psoriasis and 86 (41.3%) mild psoriasis. The higher incidence of metabolic syndrome in patients with psoriasis is evidenced by the Pearson Chi-Square test, where p [0.001. The association of metabolic syndrome in patients with psoriasis is evident. The more severe the psoriasis, the more likely it is to develop metabolic syndrome.

scholarly journals Copula-Based Bayesian Reliability Analysis of a Product of a Probability and a Frequency Model for Parallel Systems When Components Are Dependent

2021 ◽  
Vol 11 (4) ◽  
pp. 1697
Author(s):  
Shi-Woei Lin ◽  
Tapiwa Blessing Matanhire ◽  
Yi-Ting Liu
Keyword(s):  
System Reliability ◽  
Classification Tree ◽  
Parallel Systems ◽  
Reliability Model ◽  
Key Factors ◽  
Frequency Model ◽  
Independent Components ◽  
Aggregate Analysis ◽  
Dependence Assumption ◽  
Disaggregate Analysis

While the dependence assumption among the components is naturally important in evaluating the reliability of a system, studies investigating the issues of aggregation errors in Bayesian reliability analyses have been focused mainly on systems with independent components. This study developed a copula-based Bayesian reliability model to formulate dependency between components of a parallel system and to estimate the failure rate of the system. In particular, we integrated Monte Carlo simulation and classification tree learning to identify key factors that affect the magnitude of errors in the estimation of posterior means of system reliability (for different Bayesian analysis approaches—aggregate analysis, disaggregate analysis, and simplified disaggregate analysis) to provide important guidelines for choosing the most appropriate approach for analyzing a model of products of a probability and a frequency for parallel systems with dependent components.

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