NDLI: Measuring availability indexes with small samples for component and network reliability using the Sahinoglu-Libby probability model

Content Provider	IEEE Xplore Digital Library
Author	Sahinoglu, M. Libby, D.L. Das, S.R.
Copyright Year	1963
Abstract	With the advances in pervasive computing and wireless networks, the quantitative measurements of component and network availability have become a challenging task, especially in the event of often encountered insufficient failure and repair data. It is well recognized that the Forced Outage Ratio (FOR) of an embedded hardware component is defined as the failure rate divided by the sum of the failure and the repair rates; or FOR is the operating time divided by the total exposure time. However, it is also well documented that FOR is not a constant but is a random variable. The probability density function (pdf) of the FOR is the Sahinoglu-Libby (SL) probability model, named after the originators if certain underlying assumptions hold. The SL pdf is the generalized three-parameter Beta distribution (G3B). The failure and repair rates are taken to be the generalized Gamma variables where the corresponding shape and scale parameters, respectively, are not identical. The SL model is shown to default to that of a standard two-parameter Beta pdf when the shape parameters are identical. Decision Theoretic (Bayesian) solutions are employed to compute small-sample Bayesian estimators by using informative and noninformative priors for the component failure and repair rates with respect to three definitions of loss functions. These estimators for component availability are then propagated to calculate the network expected input-output or source-target (s-t) availability for four different fundamental networks given as examples. The proposed method is superior to using a deterministic way of estimating availability simply by dividing total up-time by exposure time. Various examples will illustrate the validity of this technique to avoid over- or underestimation of availability when only small samples or insufficient data exist for the historical lifecycles of components and networks.
Sponsorship	IEEE Instrumentation and Measurement Society Bureau international des poids et mesures IEEE International Bureau of Weights and Measures (BIPM) URSI NBS Agency Ind. Sci. & Technol., Min. Int. Trade & Ind. Sci. Council Japan International Union of Pure and Applied Physics (IUPAP) NIST NRCC National Conference of Standards Laboratories (NCSL) Allen Osborne Associates Andeen-Hagerling, Inc. Ballantine Lab. Inc. Clarke-Hess Communications Research Corp. Fluke Corporation Guildline Instruments Hewlett-Packard Co. Julie Res. Lab. Inc. Keithley Instruments, Inc. Measurements International Ltd. Canada QuadTech Inc. Quantum Design Inc. Rotek Instrum. Corp. Tegam Inc. Tektronix Inc
Starting Page	1283
Ending Page	1295
Page Count	13
File Size	1084107
File Format	PDF
ISSN	00189456
Volume Number	54
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Availability Random variables Shape Probability density function Hardware Bayesian methods Maximum likelihood estimation Pervasive computing Wireless networks Information technology source–target (s–t) availability Bayes beta gamma generalized three-parameter Beta distribution (G3B) informative loss Sahinoglu–Libby (SL)
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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