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C. Mukhopadhyay (2006)
Maximum likelihood analysis of masked series system lifetime dataJournal of Statistical Planning and Inference, 136
D. Lin, F. Guess (1994)
System life data analysis with dependent partial knowledge on the exact cause of system failureMicroelectronics Reliability, 34
G. Dinse (1982)
Nonparametric estimation for partially-complete time and type of failure data.Biometrics, 38 2
H. David (1980)
The theory of competing risks
R. Herman, Rusi Patell (1971)
Maximum Likelihood Estimation For Multi-Risk ModelTechnometrics, 13
L. Baxter, M. Tortorella (1994)
Dealing with real field reliability data: circumventing incompleteness by modeling and iterationProceedings of Annual Reliability and Maintainability Symposium (RAMS)
David Lunn, Andrew Thomas, N. Best, D. Spiegelhalter (2000)
WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibilityStatistics and Computing, 10
Author Wu, F. BYC., WU Jeff (1983)
ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHMAnnals of Statistics, 11
A. Peterson (1977)
Expressing the Kaplan-Meier estimator as a function of empirical subsurvival functionsJournal of the American Statistical Association, 72
J. Usher, T. Hodgson (1988)
Maximum likelihood analysis of component reliability using masked system life-test dataIEEE Transactions on Reliability, 37
J. Lawless (1983)
Statistical Models and Methods for Lifetime Data
A. Dempster, N. Laird, D. Rubin (1977)
Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
N. Breslow, D. Cox, D. Oakes (1985)
Analysis of Survival Data.Biometrics, 41
P.C. Carlin, T.A. Louis
Bayes and Empirical Bayes Mehtods for Data Analysis
M. Moeschberger, H. David (1971)
Life Tests under Competing Causes of Failure and the Theory of Competing RisksBiometrics, 27
D.E. Hutto
Analysis of reliability using real masked system life data
S. Ross (1977)
A First Course in Probability
J. Usher, S. Alexander, J. Thompson (1990)
System reliability prediction based on historical dataQuality and Reliability Engineering International, 6
J. Usher (1996)
Weibull component reliability-prediction in the presence of masked dataIEEE Trans. Reliab., 45
N.A.S. Command
Technical Manual Intermediate Maintenance with Illustrated Parts Breakdown Power Supply PP‐6616/APS‐120
M. Hamada, Changbao Wu (1995)
Analysis of Censored Data from Fractionated Experiments: A Bayesian ApproachJournal of the American Statistical Association, 90
E. Kaplan, P. Meier (1958)
Nonparametric Estimation from Incomplete ObservationsJournal of the American Statistical Association, 53
J. Usher (1992)
A computer program for component life estimation using multiply censored system life dataComputers & Industrial Engineering, 22
J. McCool (1976)
Estimation of Weibull Parameters With Competing-Mode CensoringIEEE Transactions on Reliability, R-25
J. Usher, F. Guess (1989)
An iterative approach for estimating component reliability from masked system life dataQuality and Reliability Engineering International, 5
D. Lin, J. Usher, F. Guess (1996)
Bayes estimation of component-reliability from masked system-life dataIEEE Trans. Reliab., 45
D. Lin, J. Usher, F. Guess (1993)
Exact maximum likelihood estimation using masked system dataIEEE Transactions on Reliability, 42
S. Basu, P. Asit, C. Mukhopadhyay (1999)
Bayesian analysis for masked system failure data using non-identical Weibull modelsJournal of Statistical Planning and Inference, 78
B. Reiser, B. Flehinger, A. Conn (1996)
Estimating component-defect probability from masked system success/failure dataIEEE Trans. Reliab., 45
M. Crowder (1991)
Statistical Analysis of Reliability Data
B. Turnbull (1974)
Nonparametric Estimation of a Survivorship Function with Doubly Censored DataJournal of the American Statistical Association, 69
J. Usher (1993)
Analysis of circuit-pack component reliabilityAnnual Reliability and Maintainability Symposium 1993 Proceedings
Ian Somerville, D. Dietrich, T. Mazzuchi (1997)
Bayesian reliability analysis using the Dirichlet prior distribution with emphasis on accelerated life testing run in random orderNonlinear Analysis-theory Methods & Applications, 30
M. Miyakawa (1984)
Analysis of Incomplete Data in Competing Risks ModelIEEE Transactions on Reliability, R-33
W. Nelson (1983)
Applied life data analysis
N. Doganaksoy (1991)
Interval estimation from censored and masked system-failure dataIEEE Transactions on Reliability, 40
D. Bartholomew (1957)
A Problem in Life TestingJournal of the American Statistical Association, 52
J. Luxhøj, Huan-Jyh Shyur (1995)
Reliability curve fitting for aging helicopter componentsReliability Engineering & System Safety, 48
F. Guess, J. Usher, T. Hodgson (1991)
Estimating system and component reliabilities under partial information on cause of failureJournal of Statistical Planning and Inference, 29
S. Basu, Ananda Sen, M. Banerjee (2003)
Bayesian analysis of competing risks with partially masked cause of failureJournal of the Royal Statistical Society: Series C (Applied Statistics), 52
Purpose – The purpose of this paper is to provide maintenance personnel with a methodology for using masked field reliability data to determine the probability of each subassembly failure. Design/methodology/approach – The paper compares an iterative maximum likelihood estimation method and a Bayesian methodology for handling masked data collected from 227 identical radar power supplies. The power supply consists of several subassemblies hereafter referred to as shop replaceable assemblies (SRAs). Findings – The study examined two approaches for dealing with masking, an iterative maximum likelihood estimate procedure, IMLEP, and a Bayesian approach implemented with the application WinBUGS. It indicates that the performances of IMLEP and WinBUGS in estimating the parameters of the SRA distribution under no masking conditions are similar. IMLEP and WinBUGS also provide similar results under masking conditions. However, the study indicates that WinBUGS may perform better than IMLEP when the competing risk responsible for a failure represents a smaller total percentage of the total failures. Future study to confirm this conclusion by expanding the number of SRAs into which the item under study is organized is required. Research limitations/implications – If an item is considered to be comprised of various subassemblies and the failure of the first subassembly causes the item to fail, then the item is referred to as a series system in the literature. If the probability of a each subassembly failure is statistically independent then the item can be represented by a competing risk model and the probability distributions of the subassemblies can be ascertained from the item's failure data. When the item's cause of failure is not known, the data are referred to in the literature as being masked. Since competing risk theory requires a cause of failure and a time of failure, any masked data must be addressed in the competing risk model. Practical implications – This study indicates that competing risk theory can be applied to the equipment field failure data to determine a SRA's probability of failure and thereby provide an efficient sequence of replacing suspect failed SRAs. Originality/value – The analysis of masked failure data is an important area that has had only limited study in the literature due to the availability of failure data. This paper contributes to the research by providing the complete historical equipment usage data for the item under study gathered over a time frame of approximately seven years.
International Journal of Quality & Reliability Management – Emerald Publishing
Published: Jul 31, 2009
Keywords: Maintenance reliability; Data analysis; Risk analysis; Failure (mechanical)
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