NDLI: Comparison of Parametric Models for Estimating Maintenance Times from Small Samples

Content Provider	IEEE Xplore Digital Library
Author	Borgefors, G. Hjorth, U.
Copyright Year	1963
Abstract	The estimation of optimal preventive maintenance (PM) times is considered. When failure and PM cause the only costs of interest, and the steady state cost-rate is to be minimized, very small samples of times to failure can often be effectively used. In particular, with only 10 failures the mean extra cost, due to statistical uncertainty, is in many realistic cases just a small fraction of the true optimal cost. When the ratio of failure costs to PM costs is high, the gain of assuming a correct parametric model instead of an empirical one can be substantial when only a small sample is available. On the other hand, assuming a wrong parametric model can give a much higher extra cost than the empirical model. In this paper we give Monte Carlo results comparing empirical estimation with maximum likelihood estimation based on correct and wrong parametric assumptions. The study covers: three Weibull distributions, two bathtub distributions, one Gaussian distribution. Sample sizes: 10 and 50, with and without censoring. Failure cost to PM cost ratios: 4 and 50.
Sponsorship	IEEE Reliability Society
Starting Page	375
Ending Page	380
Page Count	6
File Size	1043544
File Format	PDF
ISSN	00189529
Volume Number	R-30
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1981-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parametric statistics Costs Maximum likelihood estimation Statistical distributions Preventive maintenance Steady-state Weibull distribution Aging Parameter estimation Hazards Bathtub shaped hazard rate Optimization Age replacement Maximum likelihood Empirical estimation
Content Type	Text
Resource Type	Article
Subject	Safety, Risk, Reliability and Quality Electrical and Electronic Engineering

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