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 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.

Modeling and Uncertainty Estimation of Wind Power Curve Based on Recorded Field Data

2014 ◽  
Vol 986-987 ◽  
pp. 694-697 ◽  
Author(s):  
Peng Lin ◽  
Shu Qiang Zhao
Keyword(s):  
Confidence Interval ◽  
Wind Power ◽  
Field Data ◽  
Estimation Method ◽  
Interval Estimation ◽  
Uncertainty Estimation ◽  
Statistical Characteristics ◽  
Power Curve ◽  
Speed Level ◽  
Confidence Interval Estimation

Wind power curve of wind turbine has great importance in the prediction of wind power. The measured wind power curve is drawn by method of bins based on recorded field data; the uncertainty factors of the wind power curve is analyzed, and a non-parametric confidence interval estimation method is proposed based on analyzing the statistical characteristics of the data distribution. By means of the method, a probability density function model for wind power in each wind speed level is established, and the uncertainty estimation confidence interval of wind power curve is obtained on the basis of deterministic estimation. The example analysis proves the efficiency and feasibility of the method proposed in this paper.

scholarly journals Reliability Parameter Interval Estimation of NC Machine Tools considering Working Conditions

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Li Hongzhou ◽  
Sun Lixia
Keyword(s):  
Working Conditions ◽  
Machine Tools ◽  
Estimation Method ◽  
Interval Estimation ◽  
Point Estimation ◽  
Test Field ◽  
Reliability Parameter ◽  
Nc Machine ◽  
Nc Machine Tools ◽  
Parameter Interval

Aiming at the problem that the parameter interval estimation of NC machine tool’s reliability model considering working conditions established by Hongzhou is difficult to implement, given that it has several independent variables, an improved interval estimation method based on Bootstrap is proposed. Firstly, the two-step estimation method was used to calculate the point estimation of NC machine tool’s reliability parameter in test field, based on which B resamplings are generated based on the point estimation. The reliability parameter’s point estimation of the resamplings was obtained by maximum likelihood estimation. Permutation of B point estimations was made in ascending order and the interval estimations were obtained by the α quantile of the permutation. Case study indicated that the location and length of the interval estimation of NC machine tools’ reliability parameter, under different levels of working condition covariates, vary obviously.

A Confidence Interval Estimation Method for Process Capability Index with Bias

2014 ◽  
Vol 1039 ◽  
pp. 622-626
Author(s):  
Zai Fang Zhang ◽  
Xiao Song Wu ◽  
Rui Miao ◽  
Bei Xin Xia
Keyword(s):  
Confidence Interval ◽  
Manufacturing Industry ◽  
Research Work ◽  
Estimation Method ◽  
Interval Estimation ◽  
Process Capability ◽  
Process Capability Index ◽  
Management Tool ◽  
Capability Index ◽  
Confidence Interval Estimation

Process capability index (PCI) has been widely applied in manufacturing industry as an effective management tool for quality evaluation and improvement, whose calculation in most existing research work is premised on the assumption that there exists no bias. In this paper, the bias of gauge which exerts an effect on the calculation of PCI is indicated inevitable. The influence on PCI caused by the bias is analyzed by constructing a comparative ratio R between the empirical process capability index and the PCI. A confidence interval estimation method is proposed to solve the underestimation problem of PCI.

scholarly journals Confidence interval estimation of gamma distribution lifetime data using score and bootstrap methods

2021 ◽  
Vol 4 (1) ◽  
pp. 93-100
Author(s):  
Larasati Larasati ◽  
Rudi Ruswandi ◽  
Fitriani Fitriani
Keyword(s):  
Confidence Interval ◽  
Gamma Distribution ◽  
Bootstrap Method ◽  
Estimation Method ◽  
Interval Estimation ◽  
Likelihood Estimation ◽  
Aircraft Engine ◽  
Estimation Of Parameters ◽  
Engine Cooling ◽  
Confidence Interval Estimation

Confidence interval estimation of parameters determines the value interval, which is calculated based on statistical measurements and has specific estimates probability that contains the actual parameters. A method is needed to estimate the parameters' confidence interval, and the methods used are the Score method and the Bootstrap method. This study aims to estimate parameters by using the maximum likelihood estimation method and analyze the reliability of the aircraft engine cooling system's lifetime that follows the Gamma Distribution, and estimate the confidence interval of the parameters.

scholarly journals Common Medical and Statistical Problems: The Dilemma of the Sample Size Calculation for Sensitivity and Specificity Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1258
Author(s):  
M. Rosário Oliveira ◽  
Ana Subtil ◽  
Luzia Gonçalves
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Sample Size Calculation ◽  
Estimation Method ◽  
Interval Estimation ◽  
Sample Size Determination ◽  
Estimation Methods ◽  
Sample Sizes ◽  
Expected Length

Sample size calculation in biomedical practice is typically based on the problematic Wald method for a binomial proportion, with potentially dangerous consequences. This work highlights the need of incorporating the concept of conditional probability in sample size determination to avoid reduced sample sizes that lead to inadequate confidence intervals. Therefore, new definitions are proposed for coverage probability and expected length of confidence intervals for conditional probabilities, like sensitivity and specificity. The new definitions were used to assess seven confidence interval estimation methods. In order to determine the sample size, two procedures—an optimal one, based on the new definitions, and an approximation—were developed for each estimation method. Our findings confirm the similarity of the approximated sample sizes to the optimal ones. R code is provided to disseminate these methodological advances and translate them into biomedical practice.

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