NDLI: Accuracy of Univariate, Bivariate, and a `Modified Double Monte Carlo' Technique for Finding Lower Confidence Limits of System Reliability

Content Provider	IEEE Xplore Digital Library
Author	Depuy, Kathleen M. Hobbs, Jon R. Moore, Albert H. Johnston, J.W.
Copyright Year	1963
Abstract	The purpose of this paper is to compare the accuracy of two asymptotic (univariate and bivariate) techniques and a modified double Monte Carlo technique to obtain system reliability s-confidence limits from component test data modeled by the 2-parameter Weibull distribution. The bivariate technique assumes that the maximum likelihood estimators of the component shape and scale parameter have their asymptotic bivariate normal distribution. The univariate technique assumes that the component reliability estimates have their asymptotic s-normal distribution. A variation of the univariate technique using the beta distribution as the distribution of the component reliability estimates is also examined. The analysis reveals that: 1. The bivariate technique is generally superior to the other asymptotic techniques for the four system-configurations examined. 2. The accuracy of each technique increases as the component test-data sample-size increases, as anticipated. 3. For low system-reliability all four techniques are close in their performance. 4. The s-confidence intervals are in general s-biased (upward), that is the actual s-confidence levels are lower than desired. 5. When sample size reaches 30, the bivariate and modified double Monte Carlo techniques are comparable for three of four systems studied. 6. As sample-size for component test-data increases, the accuracy of the double Monte Carlo method increases more rapidly than other methods and it can be used in a much wider variety of situations.
Sponsorship	IEEE Reliability Society
Starting Page	474
Ending Page	477
Page Count	4
File Size	721839
File Format	PDF
ISSN	00189529
Volume Number	R-31
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1982-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Reliability System testing Shape Military computing Weibull distribution Statistical distributions Springs Probability Statistical analysis Monte Carlo s-Confidence limit
Content Type	Text
Resource Type	Article
Subject	Safety, Risk, Reliability and Quality Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Monte-Carlo Technique for Estimating Lower Confidence Limits on System Reliability Using Pass-Fail Data

Approximate Lower Confidence Limits for the Weibull Reliability Function

Comparison of Monte Carlo Techniques for Obtaining System-Reliability Confidence Limits

Confidence Intervals and Tests of Hypotheses for the Reliability of a 2-Parameter Weibull System

Confidence Intervals for Comparing Two Life Distributions

Tables for Exact Lower Confidence Limits for Reliability and Quantiles, Based on Least-Squares Estimators of Weibull Parameters

A Modified Monte Carlo Technique for Confidence Limits of System Reliability Using Pass-Fail Data

A Comparison of Three Methods for Calculating Lower Confidence Limits on System Reliability Using Binomial Component Data

A UNIVARIATE MONTE CARLO TECHNIQUE TO APPROXIMATE RELIABILITY CONFIDENCE LIMITS OF SYSTEMS WITH COMPONENTS CHARACTERIZED BY THE WEIBULL DISTRIBUTION (1979)

Accuracy of Univariate, Bivariate, and a `Modified Double Monte Carlo' Technique for Finding Lower Confidence Limits of System Reliability

Similar Documents

A Monte-Carlo Technique for Estimating Lower Confidence Limits on System Reliability Using Pass-Fail Data

Approximate Lower Confidence Limits for the Weibull Reliability Function

Comparison of Monte Carlo Techniques for Obtaining System-Reliability Confidence Limits

Confidence Intervals and Tests of Hypotheses for the Reliability of a 2-Parameter Weibull System

Confidence Intervals for Comparing Two Life Distributions

Tables for Exact Lower Confidence Limits for Reliability and Quantiles, Based on Least-Squares Estimators of Weibull Parameters

A Modified Monte Carlo Technique for Confidence Limits of System Reliability Using Pass-Fail Data

A Comparison of Three Methods for Calculating Lower Confidence Limits on System Reliability Using Binomial Component Data

A UNIVARIATE MONTE CARLO TECHNIQUE TO APPROXIMATE RELIABILITY CONFIDENCE LIMITS OF SYSTEMS WITH COMPONENTS CHARACTERIZED BY THE WEIBULL DISTRIBUTION (1979)

Accuracy of Univariate, Bivariate, and a `Modified Double Monte Carlo' Technique for Finding Lower Confidence Limits of System Reliability