1、IEEE Std 1413.1-2002IEEE Standards1413.1TMIEEE Guide for Selecting and UsingReliability Predictions Based onIEEE 1413Published by The Institute of Electrical and Electronics Engineers, Inc.3 Park Avenue, New York, NY 10016-5997, USA19 February 2003IEEE Standards Coordinating Committee 37IEEE Standar
2、ds Coordinating Committee 37 onReliability PredictionIEEE StandardsPrint: SH95020PDF: SS95020The Institute of Electrical and Electronics Engineers, Inc.3 Park Avenue, New York, NY 10016-5997, USACopyright 2003 by the Institute of Electrical and Electronics Engineers, Inc.All rights reserved. Publish
3、ed 19 February 2003. Printed in the United States of America.IEEE is a registered trademarks in the U.S. Patent +1 978 750 8400. Permission to photocopy portions of any individual standard for educationalclassroom use can also be obtained through the Copyright Clearance Center.Note: Attention is cal
4、led to the possibility that implementation of this standard may require use of subject mat-ter covered by patent rights. By publication of this standard, no position is taken with respect to the existence orvalidity of any patent rights in connection therewith. The IEEE shall not be responsible for
5、identifying patentsfor which a license may be required by an IEEE standard or for conducting inquiries into the legal validity orscope of those patents that are brought to its attention.Copyright 2003 IEEE. All rights reserved.iiiIntroduction(This introduction is not part of IEEE Std 1413.1-2002, IE
6、EE Guide for Selecting and Using Reliability PredictionsBased on IEEE 1413.)IEEE Std 1413-1998, IEEE Standard Methodology for Reliability Predictions and Assessment for ElectronicSystems and Equipment, provides a framework for reliability prediction procedures for electronic equipmentat all levels.
7、This guide is a supporting document for IEEE Std 1413-1998. This guide describes a wide vari-ety of hardware reliability prediction methodologies. The scope of this guide is processes and methodologies for conducting reliability predictions for electronicsystems and equipment. This guide focuses on
8、hardware reliability prediction methodologies, and specifi-cally excludes software reliability, availability and maintainability, human reliability, and proprietary reli-ability prediction data and methodologies. These topics may be the subjects for future IEEE 1413 guides.The purpose of this guide
9、is to assist in the selection and use of reliability prediction methodologies satisfy-ing IEEE Std 1413. The guide also describes the appropriate factors and criteria to consider when selectingreliability prediction methodologies.ParticipantsAt the time this standard was completed, the Reliability P
10、rediction Standard Development Working Grouphad the following membership:Michael Pecht,Chair Other contributors who aided in the development of this standard by providing direction and attending meet-ings were as follows:The following members of the balloting committee voted on this standard. Ballot
11、ers may have voted forapproval, disapproval, or abstention.Gary BuchananJerry L. CartwrightDr. Victor ChienDr. Vladimir CrkDr. Diganta DasDan N. DonahoeJon G. ElerathLou GulloJeff W. HarmsHarold L. HartTyrone JacksonDr. Aridaman Jain Yvonne Lord Jack ShermanThomas J. StadtermanDr. Alan WoodDr. Glenn
12、 BlackwellJens BrabandBill F. CarpenterHelen CheungLloyd CondraDr. Michael J. CushingDr. Krishna DarbhaDr. Abhijit DasguptaTony DiVentiSheri ElliottDr. Ralph EvansDiego GutierreEdward B. HakimPatrick HetheringtonZhenya HuangNino IngegneriMargaret JacksonDr. Samuel KeeneDr. Dingjun LiStephen MageeDr.
13、 Michael OstermanArun RamakrishnanJack RemezMathew SamuelKevin SilkeJohn W. SullivanRicky ValentinNancy Neeld YouensDr. Vladimir CrkDr. Michael J. CushingDr. Diganta DasRichard L. DoyleJon G. ElerathHarold L HartDennis R. HoffmanDr. Aridaman JainJack ShermanThomas J. StadtermanRicky ValentinDr. Alan
14、 WoodivCopyright 2003 IEEE. All rights reserved.When the IEEE-SA Standards Board approved this standard on 12 September 2002, it had the followingmembership:James T. Carlo,ChairJames H. Gurney,Vice ChairJudith Gorman,Secretary*Member EmeritusAlso included are the following nonvoting IEEE-SA Standard
15、s Board liaisons:Alan Cookson, NIST RepresentativeSatish K. Aggarwal, NRC RepresentativeAndrew IckowiczIEEE Standards Project EditorSid BennettH. Stephen BergerClyde R. CampRichard DeBlasioHarold E. EpsteinJulian Forster*Howard M. FrazierToshio FukudaArnold M. GreenspanRaymond HapemanDonald M. Heirm
16、anRichard H. HulettLowell G. JohnsonJoseph L. Koepfinger*Peter H. LipsNader MehravariDaleep C. MohlaWilliam J. MoylanMalcolm V. ThadenGeoffrey O. ThompsonHoward L. WolfmanDon WrightCopyright 2003 IEEE. All rights reserved.vContents1. Overview 11.1 Scope 11.2 Purpose. 11.3 Glossary . 11.4 Contents .
17、12. References 23. Definitions, abbreviations, and acronyms 83.1 Definitions 83.2 Abbreviations and acronyms 94. Background 104.1 Basic concepts and definitions. 104.2 Reliability prediction uses and timing .164.3 Considerations for selecting reliability prediction methods 185. Reliability predictio
18、n methods. 185.1 Engineering information assessment . 195.2 Predictions based on field data 235.3 Predictions based on test data 335.4 Reliability predictions based on stress and damage models 415.5 Reliability prediction based on handbooks 495.6 Assessment of reliability prediction methodologies ba
19、sed on IEEE 1413 criteria 556. System reliability models. 676.1 Reliability block diagram. 686.2 Fault-tree analysis (FTA). 766.3 Reliability of repairable systems 776.4 Monte Carlo simulation . 80Annex A (informative) Statistical data analysis . 83Annex B (informative) Bibliography 90Copyright 2003
20、 IEEE. All rights reserved.1IEEE Guide for Selecting and Using Reliability Predictions Based on IEEE 14131. OverviewIEEE Std 1413-1998 B51provides a framework for reliability prediction procedures for electronic equip-ment at all levels. This guide is a supporting document for IEEE Std 1413-1998. Th
21、is guide describes a widevariety of hardware reliability prediction methodologies.1.1 ScopeThe scope of this guide is processes and methodologies for conducting reliability predictions for electronicsystems and equipment. This guide focuses on hardware reliability prediction methodologies, and speci
22、fi-cally excludes software reliability, availability and maintainability, human reliability, and proprietaryreliability prediction data and methodologies. These topics may be the subjects for additional future IEEEguides supporting IEEE Std 1413-1998.1.2 PurposeThe purpose of this guide is to assist
23、 in the selection and use of reliability prediction methodologies satisfy-ing IEEE Std 1413-1998. The guide accomplishes this purpose by briefly describing a wide variety ofhardware reliability prediction methodologies. The guide also describes the appropriate factors and criteriato consider when se
24、lecting reliability prediction methodologies.1.3 GlossaryMany of the terms used to describe reliability prediction methodologies have multiple meanings. For exam-ple, the term reliabilityhas a specific mathematical meaning, but the word is also used to mean an entire fieldof engineering study. Claus
25、e 2 contains definitions of the terms that are used in this document, taken prima-rily from The Authoritative Dictionary of IEEE Standards Terms, Seventh EditionB3. The terms reliabilityand failureare discussed in more detail in Clause 4.1.4 ContentsClause 4 provides background information for relia
26、bility prediction methodologies. This background infor-mation includes basic reliability concepts and definitions, reliability prediction uses, reliability predictionrelationship with a system life cycle, and factors to consider when selecting reliability prediction methodol-ogies. Clause 5 describe
27、s reliability prediction methodology inputs and reliability prediction methodologiesfor components, assemblies, or subsystems. These methodologies include reliability predictions based onfield data, test data, damage simulation, and handbooks. Clause 6 describes methodologies for combiningthe predic
28、tions in Clause 5 to develop system level reliability predictions. These methodologies include reli-ability block diagrams, fault trees, repairable system techniques, and simulation.1The numbers in brackets correspond to those of the bibliography in Annex B.IEEEStd 1413.1-2002 IEEE GUIDE FOR SELECTI
29、NG AND USING2Copyright 2003 IEEE. All rights reserved.2. ReferencesThis standard shall be used in conjunction with the following publications. When the following specifica-tions are superseded by an approved revision, the revision shall apply.ABS Group, Inc., Root Cause Analysis Handbook, A Guide to
30、 Effective Incident Investigation, Risk xpxqnx, where n is the number of trials, x ranges from 0 to n, p is the probability of success and q is 1p.Exponential exp(t), where is the constant failure rate and the inverse of MTBF. Applies to middle section of idealized bathtub curve (constant failure ra
31、te).Gamma (1/!+1)texp(t/), where is the scale parameter and is the shape parameter.Lognormal (2pi t2 2)1/2exp(ln(t)/2/2, where is the mean and is the standard deviation.Normal (2pi2)1/2exp(t)/2/2, where is the mean and is the standard deviation.Poisson (t)xexp(t)/x!, where x is the number of failure
32、s and is the constant failure rate. Appropri-ate distribution for number of failures from a device population in a time period when the devices have an exponential distribution and are replaced upon failure.Weibull (/)(t/)1exp(t/), where a is the scale parameter and is the shape parameter. Infant Mo
33、rtality (shape parameter 1); constant failure rate (shape parameter = 1).TimeHazard RateDecreasing Hazard Rate (Infant Mortality)Constant Hazard Rate (Random Failures)Increasing Hazard Rate (Wearout)Hazard RateFigure 1Idealized bathtub curveIEEERELIABILITY PREDICTIONS BASED ON IEEE 1413 Std 1413.1-2
34、002Copyright 2003 IEEE. All rights reserved. 134.1.4 Measuring reliability The classical definition of reliability is the probability of providing a specified performance level for a spec-ified duration in a specified environment. This probability is a useful metric for mission-oriented, low vol-ume
35、 products such as spacecraft. However, reliability metrics for most high-volume products measure thereliability of a product population rather than the performance of a single system or a mission. Specifying asingle value such as MTBF is not sufficient for a product that exhibits a time-dependent ha
36、zard rate (i.e.,non-constant failure rate). In this case, a more appropriate metric is the probability of mission success. Thismetric may be time dependent, e.g., the probability of mission success may vary depending on the length ofthe mission, or number of cycles, e.g., the probability of mission
37、success may vary depending on the numberof uses. For “one-shot” devices, where the mission is a single event such as a warhead detonation, the proba-bility of success will be a single number. Constant rate metrics are discussed in 4.1.4.1. Probabilities of suc-cess metrics are described in 4.1.4.2.A
38、 useful reliability function is the cumulative hazard function, H(t), that can be derived from the EquationH(t) = ln(R(t). The derivative of the cumulative hazard function is the hazard rate, h(t) (see Pecht B11). 4.1.4.1 Constant rate reliability metricsThe hazard rate is the instantaneous rate of
39、failure of the product. When the hazard rate is constant, or inde-pendent of time, it is usually designated by a parameter l. Sincefor a constant failure rate, the previous equation becomes the familiar R(t) = exp( t), the exponential distri-bution. The constant parameter is usually called the const
40、ant failure rate, although sometimes the functionh(t) is also called the “failure rate,” and there are many references in the literature to increasing or decreasingfailure rates.7A constant failure rate has many useful properties, one of them is the mean value of the products life distri-bution is 1
41、/. This mean value represents the statistically expected length of time until product failure and iscommonly called the mean life, or mean-time-before/between-failure (MTBF). Another useful property ofthe constant failure rate is that it can be estimated from a population as the number of failures d
42、ivided bytime without having to fit a distribution to failure times. However, it should be noted that the exponential dis-tribution is the only distribution for which the hazard rate is a constant and that the mean life is not 1/h(t)when the hazard rate is not a constant.MTBF is sometimes misunderst
43、ood to be the life of the product rather than an expression of the constantfailure rate.8 If a product has an MTBF of 1,000,000 hours, it does not mean that the product will last thatlong (longer than the average human lifetime). Rather, it means that, on the average, one of the products willfail fo
44、r every 1,000,000 hours of product operation, i.e., if there are 1,000,000 products in the field, one ofthem will fail in one hour on the average. In this case, if product failures are truly exponentially distributed,then 63% of the products will have failed after 1,000,000 hours of operation. Produ
45、cts with truly exponen-tially distributed failures over their entire lifetime almost never occur in practice, but a constant failure rateand MTBF may be a good approximation of product failure behavior.7Since failure rate is so often implicitly interpreted as a constant parameter, the term constant
46、failure rate is used throughout this guideto mean the constant parameter l of the exponential distribution. The term hazard rate is used whenever the derivative of the hazardfunction varies with time, e.g., decreasing hazard rate or increasing hazard rate.8The use of mean time to failure (MTTF) and
47、MTBF is not standard in either reliability literature or industry practice. In some contexts,MTTF is used for non-repairable items, and MTBF is used for repairable items. In some contexts, either or both MTTF and MTBF areimplicitly assumed to imply a constant failure rate. For convenience and to hel
48、p minimize confusion in this guide, MTTF is used in con-junction with non-repairable items, MTBF is used in conjunction with repairable items, and both are used only in conjunction with aconstant failure rate. When the hazard rate is not a constant, the mean value of the reliability distribution is
49、referred to as the mean liferather than the MTBF or MTTF.Ht() ht()t t=d0t=IEEEStd 1413.1-2002 IEEE GUIDE FOR SELECTING AND USING14 Copyright 2003 IEEE. All rights reserved.If the constant rate is represented by the parameter , the mean value of the exponential distribution is 1/, asdiscussed in the preceding subclause. Therefore, constant rate metrics can be described either as a rate or asa mean life, e.g., constant failure rate or MTBF. Constant rate reliability metrics are approximations
