scholarly journals Reliability Analysis of Structural Ceramic Components Using a Three-Parameter Weibull Distribution

1992 ◽  
Author(s):  
Stephen F. Duffy ◽  
Lynn M. Powers ◽  
Alois Starlinger
Keyword(s):  
Monte Carlo Simulation ◽  
Silicon Nitride ◽  
Weibull Distribution ◽  
Reliability Analysis ◽  
Simulation Methods ◽  
Structural Ceramic ◽  
Failure Data ◽  
Regression Estimators ◽  
Ceramic Components ◽  
Sintered Silicon

This paper describes nonlinear regression estimators for the three-parameter Weibull distribution. Issues relating to the bias and invariant associated with these estimators arc examined numerically using Monte Carlo simulation methods. The estimators were used to extract parameters from sintered silicon nitride failure data. A reliability analysis was performed on a turbopump blade utilizing the three-parameter Weibull distribution and the estimates from the sintered silicon nitride data.

scholarly journals Reliability Analysis of Structural Ceramic Components Using a Three-Parameter Weibull Distribution

1993 ◽  
Vol 115 (1) ◽  
pp. 109-116 ◽  
Author(s):  
S. F. Duffy ◽  
L. M. Powers ◽  
A. Starlinger
Keyword(s):  
Monte Carlo Simulation ◽  
Silicon Nitride ◽  
Weibull Distribution ◽  
Reliability Analysis ◽  
Simulation Methods ◽  
Structural Ceramic ◽  
Failure Data ◽  
Regression Estimators ◽  
Ceramic Components ◽  
Sintered Silicon

This paper describes nonlinear regression estimators for the three-parameter Weibull distribution. Issues relating to the bias and invariance associated with these estimators are examined numerically using Monte Carlo simulation methods. The estimators were used to extract parameters from sintered silicon nitride failure data. A reliability analysis was performed on a turbopump blade utilizing the three-parameter Weibull distribution and the estimates from the sintered silicon nitride data.

scholarly journals Probability of detection of internal voids in structural ceramics using microfocus radiography

1986 ◽  
Vol 1 (3) ◽  
pp. 457-467 ◽  
Author(s):  
George Y. Baaklini ◽  
Don J. Roth
Keyword(s):  
Silicon Carbide ◽  
Silicon Nitride ◽  
Focal Spot ◽  
Probability Of Detection ◽  
Structural Ceramics ◽  
Structural Ceramic ◽  
X Ray ◽  
Low X ◽  
Statistical Results ◽  
Sintered Silicon

The reliability of microfocus x radiography for detecting internal voids in structural ceramic test specimens was statistically evaluated. The microfocus system was operated in the projection mode using low x-ray photon energies (<20 keV) and a 10 μm focal spot. The statistics were developed for implanted internal voids in green and sintered silicon carbide and silicon nitride test specimens. These statistics were compared with previously obtained statistics for implanted surface voids in similar specimens. Problems associated with void implantation and characterization are discussed. Statistical results are given as probability-of-detection curves at a 95% confidence level for voids ranging in size from 20–528 μm in diameter.

Reliability of Corroded Pipelines by Enhanced Monte Carlo Simulation

2014 ◽  
Author(s):  
Bernt J. Leira ◽  
Arvid Næss ◽  
Ole E. Brandrud Næss
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
System Reliability ◽  
Simulation Methods ◽  
Multiple Components ◽  
Corrosion Defects

The present paper addresses reliability analysis of corroding pipelines considering also system reliability effects. The analysis is performed by utilization of enhanced Monte-Carlo simulation methods which have been found to be very efficient for quantification of system reliability in the case of multiple components with arbitrary correlation levels. The examples which are studied in the paper comprise systems with corrosion defects which are both independent and correlated.

scholarly journals Parameter Estimation for Type III Discrete Weibull Distribution: A Comparative Study

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Alessandro Barbiero
Keyword(s):  
Monte Carlo Simulation ◽  
Weibull Distribution ◽  
Real Data ◽  
Discrete Distributions ◽  
Software Implementation ◽  
Discrete Models ◽  
Monte Carlo Simulation Study ◽  
Type Iii ◽  
Failure Data ◽  
Better Than

The type III discrete Weibull distribution can be used in reliability analysis for modeling failure data such as the number of shocks, cycles, or runs a component or a structure can overcome before failing. This paper describes three methods for estimating its parameters: two customary techniques and a technique particularly suitable for discrete distributions, which, in contrast to the two other techniques, provides analytical estimates, whose derivation is detailed here. The techniques’ peculiarities and practical limits are outlined. A Monte Carlo simulation study has been performed to assess the statistical performance of these methods for different parameter combinations and sample sizes and then give some indication for their mindful use. Two applications of real data are provided with the aim of showing how the type III discrete Weibull distribution can fit real data, even better than other popular discrete models, and how the inferential procedures work. A software implementation of the model is also provided.

scholarly journals RELIABILITY ANALYSIS OF PRIMARY AND PURIFICATION PUMPS IN RSG-GAS USING MONTE CARLO SIMULATION APPROACH

2019 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Entin Hartini ◽  
Hery Adrial ◽  
Santosa Pujiarta
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
System Reliability ◽  
Real Life ◽  
Random Number Generation ◽  
Successful Operation ◽  
Component Reliability ◽  
Failure Data ◽  
System Reliability Analysis

Reliability and maintenance play an important role in ensuring successful operation of a system. Reliability analysis is often used to determine the probability whether or not a system is functioning. However, limited available data and information are causing uncertainties and inaccuracies on component parameters. The purpose of this study is to conduct component/system reliability analysis using Monte Carlo simulation-based method. This method enables us to estimate the reliability of components/systems including parameter uncertainty and imprecision. It is also useful to predict and evaluate maintenance decisions related to reliability. Monte Carlo method employs random number generation based on the probability of the distribution of processed data, of which then validated with real available data to ensure the simulation condition is relatively similar to real-life condition. The data used in this research is failure data on RSG-GAS components/systems for core configuration number of 81 to 95, accumulated from year 2013 to 2018. The results show that reliability values of components JE01/AP01-02 on TTF 233.619 is 0.579 while for components KBE01/AP-01-02 in TTF 185.38 is 0.368.The component reliability value is 60%, which implies that maintenance may be performed after 225 days and 100 days for componentsJE01/AP01-02 and KBE01/AP01-02, respectively.Keywords: Reliability, Monte Carlo, Component damage, RSG-GAS

ChemInform Abstract: OXIDATION OF THIN SHEET REACTION-SINTERED SILICON NITRIDE

1978 ◽  
Vol 9 (24) ◽  
Author(s):  
J. B. WARBURTON ◽  
J. E. ANTILL ◽  
R. W. M. HAWES
Keyword(s):  
Silicon Nitride ◽  
Thin Sheet ◽  
Sintered Silicon
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