A comparative evaluation of the estimators of the weibull distribution by monte carlo simulation

1990 ◽  
Vol 36 (4) ◽  
pp. 229-241 ◽  
Author(s):  
V.P. Singh ◽  
J.F. Cruise ◽  
Ming Ma
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Weibull Distribution ◽  
Comparative Evaluation

Comparative evaluation of estimation of step gauge measurement uncertainty via Monte Carlo simulation

2019 ◽  
Vol 55 ◽  
pp. 390-396 ◽  
Author(s):  
Yuka Miura ◽  
Shoichi Nakanishi ◽  
Eiichi Higuchi ◽  
Kiyoshi Takamasu ◽  
Makoto Abe ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Measurement Uncertainty ◽  
Comparative Evaluation ◽  
Gauge Measurement

Gas Turbine Safety Improvement Through Risk Analysis

1988 ◽  
Vol 110 (2) ◽  
pp. 265-270 ◽  
Author(s):  
T. M. Crosby ◽  
G. L. Reinman
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Weibull Distribution ◽  
Gas Turbine ◽  
System Model ◽  
Weibull Analysis ◽  
System Safety ◽  
Weibull Plot ◽  
Safety Improvement

This paper is intended to provide the engineer with the information necessary to understand certain statistical methods that are used to improve system safety. It will provide an understanding of Weibull analysis, in that it describes when the Weibull distribution is appropriate, how to construct a Weibull plot, and how to use the parameters of the Weibull distribution to calculate risk. The paper will also provide the engineer with a comprehension of Monte Carlo simulation as it relates to quantifying safety risk. The basic components of Monte Carlo simulation are discussed as well as the formulation of a system model and its application in the gas turbine industry.

Influence of Applied Tensile and Bending Strains on Local and Overall Critical Current of Multifilamentary Bi2223-Composite Superconductor

2007 ◽  
Vol 539-543 ◽  
pp. 739-744
Author(s):  
Shojiro Ochiai ◽  
D. Doko ◽  
Hiroshi Okuda ◽  
Sang Soo Oh ◽  
Dong Woo Ha ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Weibull Distribution ◽  
Critical Current ◽  
Experimental Result ◽  
N Value ◽  
Local Current ◽  
Composite Superconductor ◽  
Extent Of Damage

Influence of applied tensile and bending strains on the local and overall transport critical current Ic and n-value at 77 K of multifilamentary Bi2223-composite superconductor was studied, where the n-value refers to the sharpness of the transition from super- to normal conducting state. Under both tensile and bending strains, the damage such as transverse and longitudinal cracking of the Bi2223 filaments and interfacial debonding between the filament and silver progressed. The extent of damage and accordingly the critical current was different among the local portions. The relation of the local current and n-value to overall ones was analyzed with a voltage summation model, with which the experimental result was described well. Further analysis revealed that the distribution of local critical current could be described by the Weibull distribution function and n-value could be expressed as a function of critical current. Based on these results, a Monte Carlo simulation was carried out to predict the overall critical current from the distribution of local critical current, with which the experimental results could be described.

Failure rate analysis of jaw crusher using Weibull model

2016 ◽  
Vol 231 (4) ◽  
pp. 760-772 ◽  
Author(s):  
RS Sinha ◽  
AK Mukhopadhyay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Likelihood Estimation ◽  
Jaw Crusher ◽  
Two Parameter

The primary crusher is essential equipment employed for comminuting the mineral in processing plants. Any kind of failure of its components will accordingly hinder the performance of the plant. Therefore, to minimize sudden failures, analysis should be undertaken to improve performance and operational reliability of the crushers and its components. This paper considers the methods for analyzing failure rates of a jaw crusher and its critical components application of a two-parameter Weibull distribution in a mineral processing plant fitted using statistical tests such as goodness of fit and maximum likelihood estimation. Monte Carlo simulation, analysis of variance, and artificial neural network are also applied. Two-parameter Weibull distribution is found to be the best fit distribution using Kolmogorov–Smirnov test. Maximum likelihood estimation method is used to find out the shape and scale parameter of two-parameter Weibull distribution. Monte Carlo simulation generates 40 numbers of shape parameters, scale parameters, and time. Further, 40 numbers of Weibull distribution parameters are evaluated to examine the failure rate, significant difference, and regression coefficient using ANOVA. Artificial neural network with back-propagation algorithm is used to determine R2 and is compared with analysis of variance.

Comparative evaluation of estimation of hole plate measurement uncertainty via Monte Carlo simulation

2019 ◽  
Vol 56 ◽  
pp. 496-505 ◽  
Author(s):  
Yuka Miura ◽  
Shoichi Nakanishi ◽  
Eiichi Higuchi ◽  
Kiyoshi Takamasu ◽  
Makoto Abe ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Measurement Uncertainty ◽  
Comparative Evaluation ◽  
Plate Measurement

scholarly journals Maximum logq Likelihood Estimation for Parameters of Weibull Distribution and Properties: Monte Carlo Simulation

2021 ◽  
Author(s):  
Mehmet Niyazi Cankaya ◽  
Roberto Vila
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Weibull Distribution ◽  
Mean Squared Error ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Likelihood Method ◽  
P Value ◽  
Test Statistic ◽  
Log Likelihood

Abstract The maximum logq likelihood estimation method is a generalization of the known maximum log likelihood method to overcome the problem for modeling non-identical observations ( inliers and outliers). The parameter $q$ is a tuning constant to manage the modeling capability. Weibull is a flexible and popular distribution for problems in engineering. In this study, this method is used to estimate the parameters of Weibull distribution when non-identical observations exist. Since the main idea is based on modeling capability of objective function p(x; ʘ) = logq [f(x; ʘ)], we observe that the finiteness of score functions cannot play a role in the robust estimation for inliers . The properties of Weibull distribution are examined. In the numerical experiment, the parameters of Weibull distribution are estimated by logq and its special form, log , likelihood methods if the different designs of contamination into underlying Weibull distribution are applied. The optimization is performed via genetic algorithm. The modeling competence of p(x; ʘ) and insensitiveness to non-identical observations are observed by Monte Carlo simulation. The value of $q$ can be chosen by use of the mean squared error in simulation and the $p$ -value of Kolmogorov - Smirnov test statistic used for evaluation of fitting competence. Thus, we can overcome the problem about determining of the value of $q$ for real data sets.

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