A generalization of the Weibull distribution
Four parameter generalization of Weibull distribution for more versatile life testing model
Cohen, A. C.↗
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Reliability analysis of structural ceramic components using a three-parameter Weibull distribution
Described here are nonlinear regression estimators for the three-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.
Duffy, Stephen F.↗
Software for Statistical Analysis of Weibull Distributions with Application to Gear Fatigue Data: User Manual with Verification
The Weibull distribution has been widely adopted for the statistical description and inference of fatigue data. This document provides user instructions, examples, and verification for software to analyze gear fatigue test data. The software was developed presuming the data are adequately modeled using a two-parameter Weibull distribution. The calculations are based on likelihood methods, and the approach taken is valid for data that include type 1 censoring. The software was verified by reproducing results published by others.
Krantz, Timothy L.↗
Software for Statistical Analysis of Weibull Distributions with Application to Gear Fatigue Data: User Manual with Verification
The Weibull distribution has been widely adopted for the statistical description and inference of fatigue data. This document provides user instructions, examples, and verification for software to analyze gear fatigue test data. The software was developed presuming the data are adequately modeled using a two-parameter Weibull distribution. The calculations are based on likelihood methods, and the approach taken is valid for data that include type I censoring. The software was verified by reproducing results published by others.
Kranz, Timothy L.↗
Reliability analysis of structural ceramic components using a three-parameter Weibull distribution
Described here are 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.
Duffy, Stephen F.↗
Statistical analysis of censored motion sickness latency data using the two-parameter Weibull distribution
The suitability of the two-parameter Weibull distribution for describing highly censored cat motion sickness latency data was evaluated by estimating the parameters with the maximum likelihood method and testing for goodness of fit with the Kolmogorov-Smirnov statistic. A procedure for determining confidence levels and testing for significance of the difference between Weibull parameters is described. Computer programs for these procedures may be obtained from an archival source.
Park, Won J.↗
Estimation of parameters in compound Weibull distributions
Estimating procedure using electronic digital computers applicable to experiments in which mixed Weibull distribution is statistical model
DIGITAL COMPUTER↗
Smooth empirical Bayes estimation with application to the Weibull distribution
Smooth empirical Bayes estimation with application to Weibull distribution
Bennett, G. K.↗
Lower bound on reliability for Weibull distribution when shape parameter is not estimated accurately
The mathematical relationships between the shape parameter Beta and estimates of reliability and a life limit lower bound for the two parameter Weibull distribution are investigated. It is shown that under rather general conditions, both the reliability lower bound and the allowable life limit lower bound (often called a tolerance limit) have unique global minimums over a range of Beta. Hence lower bound solutions can be obtained without assuming or estimating Beta. The existence and uniqueness of these lower bounds are proven. Some real data examples are given to show how these lower bounds can be easily established and to demonstrate their practicality. The method developed here has proven to be extremely useful when using the Weibull distribution in analysis of no-failure or few-failures data. The results are applicable not only in the aerospace industry but anywhere that system reliabilities are high.
Huang, Zhaofeng↗
Lower bound on reliability for Weibull distribution when shape parameter is not estimated accurately
The mathematical relationships between the shape parameter Beta and estimates of reliability and a life limit lower bound for the two parameter Weibull distribution are investigated. It is shown that under rather general conditions, both the reliability lower bound and the allowable life limit lower bound (often called a tolerance limit) have unique global minimums over a range of Beta. Hence lower bound solutions can be obtained without assuming or estimating Beta. The existence and uniqueness of these lower bounds are proven. Some real data examples are given to show how these lower bounds can be easily established and to demonstrate their practicality. The method developed here has proven to be extremely useful when using the Weibull distribution in analysis of no-failure or few-failures data. The results are applicable not only in the aerospace industry but anywhere that system reliabilities are high.
Huang, Zhaofeng↗
Bayesian estimation of life parameters in the Weibull distribution.
Development of a Bayesian analysis of the scale and shape parameters in the Weibull distribution and the corresponding reliability function with respect to the usual life-testing procedures. For the scale parameter theta, Bayesian estimates of theta and reliability are obtained for the uniform, exponential, and inverted gamma prior probability densities. Bhattacharya's results (1967) for the one-parameter exponential life-testing distribution are reduced to a special case of these results. A fully Bayesian analysis of both the scale and shape parameters is developed by assuming independent prior distributions; since in the latter case, analytical tractability is not possible, Bayesian estimates are obtained through a conjunction of Monte Carlo simulation and numerical-integration techniques. In both cases, a computer simulation is carried out, and a comparison is made between the Bayesian and the corresponding minimum-variance unbiased, or maximum likelihood, estimates. As expected, the Bayesian estimates are superior.
Canavos, G. C.↗
Weibull Distribution From Interval Inspection Data
Most likely failure sequence assumed. Memorandum discusses application of Weibull distribution to statistics of failures of turbopump blades. Is generalization of well known exponential random probability distribution and useful in describing component-failure modes including aging effects. Parameters found from experimental data by method of maximum likelihood.
Rheinfurth, Mario H.↗
Weibull distribution based on maximum likelihood with interval inspection data
The two Weibull parameters based upon the method of maximum likelihood are determined. The test data used were failures observed at inspection intervals. The application was the reliability analysis of the SSME oxidizer turbine blades.
Rheinfurth, M. H.↗
Least Squares Best Fit Method for the Three Parameter Weibull Distribution: Analysis of Tensile and Bend Specimens with Volume or Surface Flaw Failure
Material characterization parameters obtained from naturally flawed specimens are necessary for reliability evaluation of non-deterministic advanced ceramic structural components. The least squares best fit method is applied to the three parameter uniaxial Weibull model to obtain the material parameters from experimental tests on volume or surface flawed specimens subjected to pure tension, pure bending, four point or three point loading. Several illustrative example problems are provided.
Gross, Bernard↗
Table for estimating parameters of Weibull distribution
Table yields best linear invariant /BLI/ estimates for log of reliable life under censored life tests, permitting reliability estimations in failure analysis of items with multiple flaws. These BLI estimates have uniformly smaller expected loss than Gauss-Markov best linear unbiased estimates.
Mann, N. R.↗
Development of a Weibull posterior distribution by combining a Weibull prior with an actual failure distribution using Bayesian inference
A Bayesian inference process for system logistical planning is presented which provides a method for incorporating actual failures with prediction data for an ongoing and improving reliability estimates. The process uses the Weibull distribution, and provides a means for examining and updating logistical and maintenance support needs.
Giuntini, Michael E.↗
Program for Weibull Analysis of Fatigue Data
A Fortran computer program has been written for performing statistical analyses of fatigue-test data that are assumed to be adequately represented by a two-parameter Weibull distribution. This program calculates the following: (1) Maximum-likelihood estimates of the Weibull distribution; (2) Data for contour plots of relative likelihood for two parameters; (3) Data for contour plots of joint confidence regions; (4) Data for the profile likelihood of the Weibull-distribution parameters; (5) Data for the profile likelihood of any percentile of the distribution; and (6) Likelihood-based confidence intervals for parameters and/or percentiles of the distribution. The program can account for tests that are suspended without failure (the statistical term for such suspension of tests is "censoring"). The analytical approach followed in this program for the software is valid for type-I censoring, which is the removal of unfailed units at pre-specified times. Confidence regions and intervals are calculated by use of the likelihood-ratio method.
Krantz, Timothy L.↗
Interpretation of Zerodur® Strength Data
Recent, detailed fractographic analysis of Zerodur® strength test specimens prepared by linear grinding and etching with a proprietary process indicated low frequency damage to be the strength limiting defects Recent, detailed fractographic analysis of Zerodur® strength test specimens prepared by linear grinding and etching with a proprietary process indicated low frequency damage to be the strength limiting defects [1]. The 2-parameter Weibull distribution is usually assumed when working with ceramic and glasses such as Zerodur®, although 3-parameter behavior is occasionally considered [2]. Detailed statistical modeling of the Zerodur® strength data with the was initial perspective of a 3-parameter Weibull distribution gave unsatisfying results [3]. The subsequent fractographic investigation indicated [1] that the usual assumption of many small, random, noninteracting flaws [4] was not represented, but instead, less frequent, aligned flaws along etching ridges parallel to the grinding direction were present, Figure 1. It was thus concluded that the strength of Zerodur® as prepared was not Weibull distributed. The flaws represent a sparse flaw population relative to typical grinding damage, but an extensive occurrence (high frequency) of handling damage. We examine the type of distribution present and explain the appearance of a 3-parameter distribution.
Glass, ceramic, strength, Weibull, distribution, m↗