2012 journal riac web_2

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OF THE RELIABILIT Y INFORMATION ANALYSIS CE

The Reliability In ormation Analysis Center RIACis a DoD In ormation Analysis Center sponsored bythe De ense Technical In ormation Center

VOLUME 20, NO. 1JAN UARY 2012

02 F IFTY YEARS OF PHYSICS OF FAILURE

08 POWER SYSTEM PROGNO STICS FOR THE U.S . ARMY OH-58DHELICOPTER

23 NEW MILITARY HANDBOOK 189C, RELIABILITY GROWTHMANAGEMENT

24 RIAC ANNOUNCES MAJOR NEW PRODUCT/SERVICE RELEASES

28 DEVELOPMENT OF THE WEB-ACCESSIBLE REPOSITORY OFPHYSICS-BASED MODELS (WARP)

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This year marks the tieth anniversary since Physics o Failure(PoF) was ormally conceptualized in the rst o a series o symposiain 1962 organized by the Rome Air Development Center (RADC)1 othe US Air Force. The driving orce that established this approach toreliability was concerns in the 1940s and 1950s in US military estab-lishments regarding the reliability o electronic systems. However,mechanistic treatment o ailures had its roots in the late nineteenthcentury when, in 1870, A. Wohler summarized atigue test results onrailroad axles, and concluded that cyclic loads are more important

or determining li e than peak loads. Therea ter, much o the reli-ability work in the rst hal o the twentieth century was relatedto atigue and racture o materials ( atigue ailure was the mainconcern during World War I). For example,Basquin (1910) proposed

a log-log relationship between stress and li e (the so-called S-N)curves using Wohlers atigue test data.Gri fth (1921) introducedhis theory o racture while exploring the strength o elastic brittlematerials. Miner (1945) popularized the linear damage hypothesissuggested byPalmgren (1924) as a practical design tool in which theexpended atigue li e o metals was empirically modeled.Epstein(1948) published the statistical oundation or assessment o the li eo materials subject to racture.

1 RADC became Rome Laboratory (RL), but is now known as Air ForceResearch Laboratory (AFRL) In ormation Directorate.* Also a Pro essor at the Bar Ilan University, Ramat Gan, Israel.

Kaushik Chatterjee, Mohammad Modarres and Joseph B. BernsteinCenter or Risk and Reliability Department o Mechanical EngineeringUniversity o Maryland College Park

At the start o World War II, it was discovered that over 5airborne electronics equipment in storage was unable to mrequirements o the Air Core and Navy ( McLinn, 2011). In 195US Department o De ense (DoD) initiated an ad hoc groability o electronic equipment, which stated that to imprreliability it was essential to develop better parts, establish tive reliability requirements or parts, and collect eld ato determine the root cause o problems (Ebel, 1998). However

ormation o the Advisory Group on the Reliability o Equipment (AGREE) in August 1952 by the DoD is o ten the turning point in modern reliability engineering. The committee recommended that the high cost o ownershiplow reliability could be controlled by developing reliability

grams using stress tests such as high and low temperaturetion, and other cyclic environments to understand causes oand ways to correct them. It also recommended the developa reliability demonstration program in terms o the estimali e o equipment and the con dence associated with suchThe reliability techniques recommended by AGREE were by the DoD, and later by NASA and many other organsupplying high technology equipment. Therea ter, severaences began in the 1950s to ocus on various reliability tocon erence that warrants special mention is the Holm Coon Electrical Contacts, begun in 1955, which emphasized

FIFT Y YEARS OF PHYSICS O F FAILURE

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ity physics. This con erence established itsel over the years as theprimary source o reliability physics in ormation on connectors.

Reliability work related to atigue and racture o materials con-tinued through the 1950s and early 1960s. For example, in 1957G.R. Irwin proved that the racture o materials was due to plasticde ormation at the crack tip and generalized Gri ths theory (Irwin,1957). Between 1955 and 1963, Waloddi Weibull produced severalpublications related to modeling o atigue and creep mechanismsin which methods or evaluating atigue and creep ailure data werediscussed (Weibull, 1959). In 1961, Weibull published a book onmaterials and atigue testing while working as a consultant or theUS Air Force Materials Laboratory (Weibull, 1961). Another impor-tant milestone was the introduction o methods or predicting therate o growth o atigue cracks building on Irwins work on stressintensity actor (Paris, 1961).

Against the backdrop o the developments in mechanistic-basedli e models (particularly in the assessment o atigue and racture

ailures) and the AGREE recommendations, RADC introduced aPoF program in 1961 to address the growing complexity o militaryequipment and the consequent higher number o ailures observed.In 1962, researchers rom Bell Labs published a paper on HighStress Aging to Failure o Semiconductor Devices that justi edusing the kinetic theorys interpretation o the Arrhenius equation,a simple yet accurate ormula or the temperature dependence othe reaction rate constant as a basis or assessment o temperature-induced aging o semiconductor devices (Dodson and Howard, 1961).Later, RADC and the Armour Research Foundation o the IllinoisInstitute o Technology (now IIT Research Institute) organized the

rst PoF symposium in electronics in September 1962 in Chicago.This symposium laid the groundwork or uture research anddevelopment activities related to PoF by RADC and several otherorganizations. Numerous original papers and ideas introducing

and explaining the PoF concepts and methods were presented inthese symposia.

In one o the original PoF papers presented at the rst PoF sympo-sium,Vaccaro (1962) opined that PoF should seek to relate the un-damental physical and chemical behavior o materials (i.e., ailuremechanisms) to reliability parameters. This approach is based onthe principle that to eliminate the occurrence o ailures, it is essentialto eliminate their root causes, and to do that one must understandthe physics o the underlying ailure mechanisms involved.Davis(1962) described the need or identi ying probable ailure mecha-nisms by which components ail as a unction o time, environmen-tal and operating stresses. He also developed mathematical models

to represent these mechanisms in order to meet reliability require-ments o components. Various companies and universities conduct-ing research on ailure mechanisms were active participants. Theseincluded Raytheon, Syracuse University, and Motorola. AlthoughPoF was key to improving the design and reliability o components,higher costs in terms o acilities and manpower were identi ed asthe critical impediments to using PoF at that time (Ryerson, 1962).The various key elements o the PoF approach, such as identi ca-tion o the ailure mode, mechanism, and cause, were de ned or the

rst time in this symposium (Zierdt , 1962;Earles and Eddins, 1962).

Due to the success o the rst symposium in 1962, our Pposia were held in subsequent years (until 1966), with manypapers describing concepts related to PoF. For example,Tamburrino(1963) provided key points about the requirements o a relphysics program: e.g., materials, measurement techniques

ailure mechanisms. The need or part vendors to be kepto available knowledge in ailure physics was identi ed. also stated that any changes in pre-established part process

abrication could potentially be a key actor in inducing newmechanisms, and should be closely coordinated with reliengineers. Bretts, et al. (1963) provided accelerated test resultresistors, which they correlated with physical degradation mto estimate time to ailure. PoF was identi ed as an essentiaplanning accelerated tests as well as evaluating them.

In the third PoF symposium,Ingram (1964) described per ormcharacteristics and ailure mechanisms o a device in probterms. He suggested, Environmental and stress conditions cable to the device, and its per ormance and strength characteare expressed in the orm o multidimensional probability dtions. By joint evaluation o these probability distributions, titative estimate o the reliability o the device can be obBeau (1964) described methods or managing and assessing to the human elements in PoF. He described three classicao ailure: reliability limitation inherent in the design; redegradation caused by the actory process; and reliability detion caused by the user. According to him, the actory operthe orm o poor workmanship or operator error, introduchuman element in reliability o devices.Workman (1964) describthe ailure analysis practices ollowed by Texas Instrumthat time, and the need or incorporating in ormation gaine

ailure analysis in new reliability test design, process contrdevice design.

Shiomi(1965) introduced a generalized cumulative degradmodel or estimation and prediction o component li e un

cessive di erent stress levels.Partridge, et al. (1965) argued thvendors supplying semiconductor parts and the parts themsshould be screened based on engineering evaluations that raparts reliability. They urther stated that quali cation testwere insu cient to determine the ability o vendors to provable parts, but production procurement data rom screenin burn-in could provide su cient vendor history.Church and Roberts(1965) presented di erent causes o ailure o a compone

ailure due to accidental damage during manu acture, asstesting, storage, or ailure in service due to operating condit

ailure o another component.

Thomas (1966) used basic concepts o dimensional analysis ta general examination o mathematical models, e.g., Eyringtion. He opined that the concepts o signal, noise and dimensvariables could be used to ormulate mathematical models, plaws, and probability distributions.Schenck (1966) presented tw

orms o progressive ailure mechanisms o a commercidiode, and studied them as a unction o various stress and mment variables. Several papers were also presented that pronondestructive inspection and screening procedures based onwhich later ormed the basis or prognosis and health mana

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concepts. For example,Gill and Workman (1966) presented a reli-ability screening procedure (consisting o destructive tests and non-destructive inspections) based on identi ying ailure mechanismsresulting rom high-stress tests and ailure analysis.Potter andSawyer (1966) presented an optical scanning-based nondestructivetechnique to study various semiconductor device phenomena andidenti y causes o anomalous device behavior in order to improvedevice reliability.

From 1967 on, IEEE sponsored the Reliability Physics Symposium(IRPS) to present a wide range o PoF related research. For example,Ryerson (1967) presented mathematical models or semiconductordiodes illustrating how ailure mechanisms, part strengths, andapplication stresses interact and a ect the ailure rate o componentparts. Keen et al. (1967) presented mechanisms o ailure in ohmicand expanded contacts, rom metal-semiconductor contacts and bonds to metallization in semiconductor devices.Payne (1967)presented a ailure mechanism or barium titanate capacitors bystudying the PoF.Frankel and Kinsolving (1970) discussed the need

or reliability testing o components or hostile environments, byrst simulating eld conditions and then developing accelerated

laboratory conditions. Hollingshead (1970) introduced a techniqueor optimizing the selection o parts or system application by reli-

ability and quality levels through systematizing the compilationand processing o necessary data. The comparative infuences oper ormance parameters such as repair cost, storage time, and costo ailure were discussed.Schwuttke (1970) showed that peripheralyield loss in silicon wa ers can be minimized whenever temperaturegradients arising during cooling o a row o wa ers are eliminated.

The IEEE IRPS continued to disseminate a plethora o knowledgeon PoF through the 1970s and 1980s. Several ailure mechanismsand mathematical models were reported or a wide range o elec-

tronic components such as capacitors, semiconductors, resistors,and interconnects. Metallization, metallurgical e ects and bondingdominated the key presentations and papers published by IRPS. Forexample,Black(1974) presented a model or predicting electromigra-tion time to ailure. Macpherson, et al. (1975) introduced the concepto ast temperature cycling as a key agent o ailure in transistormetallization.Crook (1979) presented a model or time dependentdielectric breakdown (TDDB) o semiconductors as a unction ooperational and environmental conditions and the devices physicalparameters. Lloyd (1983) presented the initial analysis o electro-migration e ects in multilevel geometries. Hieber and Pape (1984)presented a creep-rupture equation that calculates time to ruptureas a unction o applied mechanical load and temperature.Chen, et

al. (1985) presented a quantitative breakdown model or thin gateand tunneling oxides based on the physical understanding o oxide breakdown.Christou, et al. (1985) presented reliability investigationresults or high electron mobility transistor devices.Conrad, et al. (1988) presented a methodology to monitor and predict early li ereliability ailure mechanisms. While there was a noticeable declinein PoF techniques and uses during this period, the IRPS continuedPoF related publications until a resurgence o interest in PoF beganin the 1990s that has continued until today.

By the late 1980s and early 1990s, several publications on PoF-relatedresearch outside o the IEEE IRPS also appeared. For example,Pecht,

et al. (1990) advocated use o the PoF approach or reliabiment as opposed to the part count technique.Dasgupta and Pec(1991) published a series o tutorial papers to review imaterial ailure mechanisms and damage models.Engel (1993) sented ailure models or mechanical wear modes and meCushing, et al. (1993) o the U.S. Army Material Systems Activity (AMSAA) compared empirically-based reliabilittion approaches (e.g., MIL-HDBK-217) with the PoF They identi ed several limitations o MIL-HDBK-217 thaaddressed using the PoF approach.

Although studies related to PoF were published through thand 2000s, a trend towards probabilistic2 consideration oalso emerged rom the early 1990s. For example, Hu, et al. (19presented a probabilistic approach or predicting thermali e o wire bonding in microelectronics. Mendel (1996) ordescribed probabilistic PoF (PPoF) as a technique in whictistical li etime model is derived considering the PoF, and pa case or applying PPoF in design or reliability. Later Modarret al. (1999) also emphasized that prediction o ailure is a probabilistic problem due to uncertainties associated wmodels and their parameters, and with ailure-inducingthat can result rom changes in environmental, operating,conditions. Several publications related to the PPoF then a

rom the early 2000s. For example, Haggag, et al. (2000) presenPPoF approach to reliability assurance o high-per ormathat considered common de ect activation energy distribut Hand Strutt (2003) presented PPoF models or component re by considering parameter and model uncertainties. Azarkhail a Modarres (2007) presented a Bayesian ramework or physreliability models. Matik and Sruk (2008) highlighted the neePoF to be probabilistic in order to consider variations o involved in processes contributing to the occurrence o

Chatterjee and Modarres (2011) presented a PPoF approach mating tube rupture requency in advanced nuclear plangenerators that considered the PoF and various uncertainticiated with environmental conditions, geometrical and mproperties, PoF models, and model parameters.

Another important milestone in the discussion on PoF was lication in 2008 o thePhysics-o -Failure Based Handbook o Micrtronic Systems (Salemi et al.,2008) through the Reliability In oAnalysis Center (RIAC). The handbook was the rst o present an approach or microelectronic system reliabilitment and quali cation based on PoF in a sum-o -rate to account or multiple mechanisms. Another importa

ity currently underway is the development o the Web AcRepository o Physics-Based Models (WARP) under the aRIAC3. The objective o WARP is to collect and analyze tteristics o important PoF models or electronic, electromand mechanical components in order to provide a centraliz based repository accessible to researchers and engineers4.

2 Probabilistic consideration o mechanistic li e-models had its roo1940s even be ore the term PoF was introduced, when Epstein (1948) presentthe statistical oundation or li e assessment o materials ailing by 3 http://theriac.org4 Chatterjee, Modarres and Christou are directly participating in the ment o WARP

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As we celebrate ty years o PoF, RADC deserves special recogni-tion, including its chie and ounder Joseph J. Naresky, under whoseleadership PoF was rst ormally conceptualized in the symposiumon Physics o Failure in Electronics organized in 1962, with con-siderable contributions by RADCs Joseph Vaccaro. It is remarkablethat many o the original ideas introduced in these symposia con-tinue to have a signi cant impact on todays understanding o ail-ures in electronics and have o ered enduring models or estimatingli e characteristics. As was observed or the rst time in the 1962symposium, the PoF approach encompasses multiple disciplines,such as reliability engineering, physics, metallurgy, mathematicalstatistics and probability. The symposia o the 1960s provided PoFapproaches or non-destructive test methods and or improving andpredicting component reliability with limited access to mass testdata. While PoF analysis is complex and costly to apply, it providesthe strongest characterization available o reliability o components,structures and systems. As an approach or reliable product devel-opment, PoF has gained wide acceptance today in the commercialsector (e.g., at Microso t), as well as in several countries (e.g., Japan,Singapore, and Taiwan)a tribute to its strong oundation estab-lished ty years ago.

ReferencesAzarkhail, M., and Modarres, M., A Novel Bayesian Framework or Uncer-

tainty Management in Physics-Based Reliability Models,Proceedings o the ASME International Mechanical Engineering Congress and Exposition ,November 11-15, 2007, Seattle, WA

Basquin, O. H. The Exponential Law o Endurance Tests,Proceedings o American Society o Testing Materials, Vol. 10, pp: 625-630, 1910

Beau, J.F., Management o the human element in the physics o ailure,Proceedings o Third Annual Symposium on the Physics o Failure in Elec-

tronics, pp: 264-279, 1964Black, J.R. Physics o electromigration, Proceedings o 12th Annual Reli-

ability Physics Symposium, pp: 142-149, 1974Bretts, G., Kozol, J., and Lampert, H., Failure physics and accelerated

testing,Proceedings o First Annual Symposium on the Physics o Failure inElectronics, pp: 189-207, September, 1963

Chatterjee, K., and Modarres, M., A probabilistic physics-o - ailureapproach to prediction o steam generator tube rupture requency,Proceedings o the International Topical Meeting on Probabilistic Sa ety Assessment and Analysis, March 13-17, 2011, Wilmington, NC

Chen, I.C., Holland, S. and Hu, C., A quantitative physical model or time-dependent breakdown in SiO2,Proceedings o 23rd Annual ReliabilityPhysics Symposium, pp: 24-31, 1985

Christou, A., Tseng, W., Peckerar, M., Anderson,W.T., McCarthy, D.M., Buot,F.A., Campbell, A.B., and Knudson, A.R., Failure Mechanism Studyo GaAs MODFET Devices and Integrated Circuits,Proceedings o 23rd Annual Reliability Physics Symposium, pp:54-59, 1985

Church, H.F., and Roberts, B.C., Failure mechanisms o electronic compo-nents,Proceedings o Fourth Annual Symposium on the Physics o Failurein Electronics, pp: 156-178, 1965

Conrad, T.R., Mielnik, R.J., and Musolino, L.S., A test methodology to

monitor and predict early li e reliability ailure mechanisms,Proceedings o 26th Annual Reliability Physics Symposium, pp: 126-130, 1988

Crook, D.L., Method o determining reliability screens or time ddielectric breakdown,Proceedings o 17th Annual Reliability PhysicSymposium, pp: 1-7, 1979

Cushing, M.J., Mortin, D.E., Stadterman, T.J., and Malhotra, A., Cson o Electronics-Reliability Assessment Approaches,IEEE Transactions on Reliability, Vol. 42, Issue 4, December 1993

Dasgupta, A., and Pecht, M., Material ailure mechanisms and models,IEEE Transactions on Reliability, Vol. 40, Issue 5, pp: 531-December 1991

Davis, H., Introduction,Proceedings o the First Annual Symposium oPhysics o Failure in Electronics, September 26-27, pp: 1-3, 1962

Dodson, G.A., and Howard, B.T., High stress aging to ailure o ductor devices,Proceedings o Seventh National Symposium on Reliabiliand Quality Control , Philadelphia, PA, January 1961

Earles, D.R., and Eddins, M.F., Reliability physics (the physics o Proceedings o First Annual Symposium on the Physics o Failure in Electroics, pp: 179-193, September 26-27, 1962

Ebel, G.H., Reliability physics in electronics: a historical view,IEEE Transactions on Reliability, Vol. 47, Issue 3, September 1998

Engel, P.A., Failure models or mechanical wear modes and mechIEEE Transactions on Reliability, Vol. 42, Issue 2, June 1993

Epstein, B., Statistical Aspects o Fracture Problems, Journal o AppliePhysics, Vol. 19, February 1948

Frankel, H., and Kinsolving, W., Reliability testing or hostile ments,Proceedings o Eighth Annual Reliability Physics Symposium,pp219, 1970

Fischer, A. H., et al., Experimental Data and Statistical Models orEM Failures, in 38th Annual Proceedings o IRPS, pp: 359-363, 2

Gill, W., and Workman, W., Reliability screening procedures or incircuits,Proceedings o First Annual Symposium on the Physics o Failuin Electronics, pp: 101-140, 1966

Gri th, A. A. The phenomena o rupture and fow in solids,PhilosophicaTransactions o the Royal Society, Vol. 221, pp: 163-198, 1921

Haggag, McMahon, Hess, Cheng, Lee, and Lyding, A Probabilistic-o -Failure/Short-Time-Test Approach to Reliability AssuranHigh-Per ormance Chips: Models or Deep-Submicron TransiOptical Interconnects,Proceedings o IEEE Integrated Reliability Worshop, pp: 179-182, October 23-26, 2000

Hall, P.L., and Strutt, J.E., Probabilistic physics-o - ailure modelsponent reliabilities using Monte Carlo simulation and Weibull ana parametric study,Reliability Engineering & System Sa ety, Vol. 8Issue 3, pp: 233-242, June 2003

Hieber, H., and Pape, K., Li etime o bonded contacts on thin llization,Proceedings o 22nd Annual Reliability Physics Symposium, pp128-133, 1984

Hollingshead, C.O., A system oriented components selection optimtechnique,Proceedings o Eighth Annual Reliability Physics Symposiumpp: 220-225, 1970

Hu, J.M., Pecht, M., and Dasgupta, A., A Probabilistic Approach oing Thermal Fatigue Li e o Wire Bonding in Microelectronics Journa



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FIFTY YEARS OF PHYSICS OF FAILURE

o Electronic Packaging, Vol. 113, Issue 3, pp: 275-285, 1991Ingram, G.E., Prediction o Device Reliability by Mechanisms-o -Failure

Principles,Proceedings o Third Annual Symposium on the Physics o Failure in Electronics, pp: 200-209, 1964

Irwin G. R., Analysis o stresses and strains near the end o a crack travers-ing a plate, Journal o Applied Mechanics,Vol. 24, pp: 361364, 1957

Keen, R.S., Loewenstern, L.R., Schnable, G.L., Mechanisms o Contact

Failure in Semiconductor Devices,Proceedings o IEEE Sixth AnnualReliability Physics Symposium, pp: 216-233, 1967Lloyd, J.R., Electromigration-induced extrusions in multi-level technolo-

gies,Proceedings o 21st Annual Reliability Physics Symposium, pp: 208-210, 1983

Macpherson, A.C., Weisenberger, W.H., Day, H.M., and Christou, A., E ectso Fast Temperature Cycling on Aluminum and Gold Metal Systems, Proceedings o 13th Annual Reliability Physics Symposium, pp: 113-120, 1975

Matik, Z., and Sruk, V., The physics-o - ailure approach in reliability engi-neering, Proceedings o IEEE International Con erence on In ormationTechnology Inter aces, pp: 745-750, June 23-26, 2008

McLinn, J., A short history o reliability,The Journal o the Reliability In or-mation Analysis Center , January 2011

Mendel, M., The case or probabilistic physics o ailure, Chapter inReliability and Maintenance o Complex Systems, Edited by Ozekici, S.,Springer, 1996

Miner, M.A., Cumulative Damage in Fatigue, Journal o Applied Mechanics,Vol. 12, No. 3, pp: A- l59- l64, September, 1945

Modarres, M., Kaminskiy, M., and Krivtsov, V., Reliability engineering andrisk analysis: A practical guide, Marcel Dekker, New York, 1999

Naresky, J.J., Foreword,Proceedings o First Annual Symposium on the Physicso Failure in Electronics,September 26-27, 1962

Palmgren, A., Durability o ball bearings,ZDVDI , Vol. 68, Issue 14, pp:

339, 1924 (in German)Paris, P.C., Gomez, M. P. and Anderson W.E., A rational analytic theory o

atigue,The Trend in Engineering , Vol. 13, pp: 9-14, 1961Partridge, J., Hall, E.C., and Hanley, L.D., The application o ailure analysis

in procuring and screening o integrated circuits,Proceedings o Fourth Annual Symposium on the Physics o Failure in Electronics,pp: 96-139, 1965

Payne, D.A., Concerning the physics o ailure o Barium Titanate capaci-tors,Proceedings o IEEE Sixth Annual Symposium on Reliability Physics,pp: 257-264, 1967

Pecht, M., Dasgupta, A., Barker, D., Leonard, C.T., The reliability physicsapproach to ailure prediction modeling,Quality and Reliability Engi-neering International , Vol. 6, Issue 4, pp: 267-273, September/October1990

Potter, C.N., and Sawyer, D.E., Optical scanning technique or setor device screening and identi cation o sur ace and junctioena,Proceedings o Fi th Annual Symposium on the Physics o FailElectronics,pp: 37-50, 1966

Ryerson, C.M., Project control to provide or the physics o atronics,Proceedings o First Annual Symposium on the Physics o FailuElectronics, pp: 68-72, September 26-27, 1962

Ryerson, C.M., Mathematical Modeling For Predicting FailureComponent Part,Proceedings o IEEE Sixth Annual Reliability PhySymposium, pp: 10-15, 1967

Salemi, S., Yang, L., Dai, J., Qin, J., and Bernstein, J.B., Physics-o -Failure bhandbook o microelectronic systems, Reliability In ormation ACenter, Utica, NY, 2008

Schenck, J.F., Progressive ailure mechanisms o a commerdiode,Proceedings o Fi th Annual Symposium on the Physics o FailElectronics,pp: 18-35, 1966

Schwuttke, G.H., Yield problems in LSI technology,Proceedings o Ei Annual Reliability Physics Symposium, pp: 274-280, 1970

Shiomi, H., Cumulative degradation model and its application t

nent li e estimation,Proceedings o Fourth Annual Symposium onPhysics o Failure in Electronics, pp: 74-94, 1965

Tamburrino, A.L., Analysis o requirements in reliability physicProceings o First Annual Symposium on the Physics o Failure in Electronics, 189-207, September, 1963

Thomas, R.E., Some uni ying concepts in reliability physics, mmodels, and statistics,Proceedings o Fi th Annual Symposium onPhysics o Failure in Electronics,pp: 1-17, 1966

Vaccaro, J., Reliability and Physics o Failure Program at RADCProceings o First Annual Symposium on the Physics o Failure in Electronics, 4-10, September 26-27, 1962

Weibull, W., Statistical evaluation o data rom atigue and crtests, Part I: undamental concepts and general methods, WDevelopment Center, Technical Report 59-400, Sweden, Septe

Weibull, W., Fatigue testing and analysis o results,Pergamon PrLondon, 1961

Workman, W., Failure analysis techniques,Proceedings o Third AnSymposium on the Physics o Failure in Electronics,pp: 238-263, 1964

Zierdt, C.H., Diagnostic techniques in semiconductor device stres

analysis,Proceedings o First Annual Symposium on the Physics o Fain Electronics, pp: 91-97, September 26-27, 1962

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PRODUCTS OR SERVICES ADVERTISED*

*Paid advertising appears on pages 22 and 34.


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Course Description:This course was developed by the RIAC as an introduction tomaintainability engineering concepts and practices. It begins with a brieoverview o the RIAC, and because maintenance demand is driven bysystem reliability, provides an overview o basic reliability engineeringconcepts be ore proceeding into the core maintainability topics.

Section 3 begins the core content by covering basic maintainabilityconcepts and defnitions, the need or maintainable systems and theelements o a comprehensive maintainability program.

Section 4 covers the basic mathematical oundations o maintainabilityby describing various probability distributions that are commonly used tomodel system maintainability.

Section 5 describes establishing maintainability requirements at thesystem level as well as at lower levels o design. These include establishingquantitative requirements, such as mean time to repair (MTTR), as well astechniques such as quality unction deployment (QDF) that are used toensure user requirements are met.

Section 6 delves into maintainability design techniques such as allocations,predictions, human engineering and standardization.

Section 7 covers maintainability verifcation approaches, includinginspection, analytical methods and testing.

Section 8 describes trending methods to track and improve maintainabilityover time and

Section 9 discusses ensuring maintainability is not degraded by production

decisions.Section 10 provides a review and wrap-up o the course.

The course also includes two quizzes to rein orce the concepts covered.

Features & Benefts:

X 3 Days o live recorded eed including quizzes and demos, totalingover 14 Hours

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Get instant online access for only $299

View the online demo at: http://theRIAC.org

ntroduction to Maintainability EngineeringOn-Line Training Course

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POWER SYSTE M PROGNOSTI CS FOR THE U.S . ARMY OH-58DHELICOPTER

Jefrey Banks, Todd Batzel, Robert Keolian, Matt Poese, Terrance Lovell,Mitch Lebold, and Karl Reichard, Applied Research Laboratory at The Pennsylvania State University State CollegeKevin Cunningham, Bell Helicopter Textron

AbstractThe OH-58D Kiowa Warrior helicopter has been a workhorse orthe U.S. Army or decades and is projected to continue to accrue

fight hours or years to come as a highly capable plat orm appliedagainst various mission pro les. The U.S. Army is interested inthe implementation o condition based maintenance (CBM) orthis plat orm to increase operational availability o the aircra t,reduce the required number o maintenance activities and increasethe inspection interval period. The CBM methodology and theseobjectives are directly dependent upon the capability o the healthand usage monitoring system and its ability to detect diagnose andprovide an estimate o remaining use ul li e (RUL).

The Applied Research Laboratory at The Pennsylvania State Uni-versity (ARL Penn State) has developed prognostic technologies orhelicopter electrical power systems that can be integrated into an

existing on-board Health and Usage Monitoring System (HUMS).The goal o the program was to develop a capability to monitor,detect and provide an estimated remaining use ul li e RUL or thestarter/generator, battery and power inverter be ore they lead toelectrical power system ailures. The ocus o this e ort was todevelop technologies that provide actionable prognostic in orma-tion or orecasting part replacement and to extend existing timeand usage-based maintenance intervals.

1. IntroductionARL Penn State in collaboration with Bell Helicopter dadvanced diagnostic and prognostic health manag

technologies or helicopter electrical power system comthat can be integrated into existing on-board Health andMonitoring Systems (HUMS). The program was supportedthe Aviation Applied Technology Directorate (AATD) OpSupport and Sustainment Technology (OSST) program t jointly unded by AATD and Bell Helicopter. The objectiveplat orms or this program were the OH-58D Kiowa WarriBell 407, although this paper will ocus on the OH-58D pl1

The goal o the program was to develop an embedded cto monitor, detect, ault isolate and provide a remaining uestimate or starter/generator, battery and power inverter

The intent was to develop technologies that provide acadvanced diagnostic and prognostic in ormation to hemaintainers or orecasting part replacement and to extenmaintenance intervals.

The development o helicopter electrical power systemmanagement technologies involved ve phases. The rst pthe process or all o the technology areas consisted o co

ailure modes, and e ects analysis (FMEA) or the OH

1 978-1-4244-7351-9/11/$26.00 2011 IEEE.IEEEAC paper #1654, Version 2, Updated October 26, 2010 Article is with permission o the authors.



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Warrior (su cient in ormation was not available or a separateanalysis o the Bell 407 helicopter). The second phase consisted oconducting operational helicopter eld tests to collect data to enablea better understanding o the power system unction and dynamics.This data was use ul or test bed design, algorithm developmentand plat orm integration. The third phase in the process involvedthe design o technology development approaches that includedthe creation o component models, evaluation o the applicationo sensors, data processing requirements, building o test benches,laboratory data gathering, and algorithm development. The ourthphase o the e ort consisted o the application o the developedtechnologies to laboratory test benches or technology testing,validation and re nement. The th phase involved the designand development o embedded hardware/so tware and a healthmanagement in ormation inter ace.

2. Power System FMEA ResultsIn order to provide a ocus or the technology development e ort,a FMEA was conducted or each power system component. Theactivities per ormed or this analysis included:

Acquired maintenance data and conducted interviews withthe OH-58D contract maintenance personnel at the AviationCenter Logistics Command at Fort Rucker.

Conducted interviews with Kiowa Warrior MaintenanceTest Flight Examiner (United Stated Army Sa ety andStandardization Directorate (USASSD)) tasked to inspectmaintenance operations in all U.S. Army OH-58D units.

Conducted interviews with Army employee who repairsKiowa Warrior inverters at Tobyhanna Army Depot.

Based on the FMEA results or each power system component, each

health management technology development team ocused theirdevelopment on the most critical ailure modes.

Starter-Generator FMEA Results

The starter-generator is an integrated dual purpose device thatprovides the ability to start the helicopter turbine engine withon-aircra t battery power or external auxiliary power. The generatorprovides the ability to continuously create DC electrical poweron-aircra t during fight operations. This power is provided to theBattery-Generator Bus and the 28VDC Essential Bus that supportsvarious avionics and electronic systems. The FMEA results orthis device only provided a limited set o ailure data to assess the

most dominant ailure modes. Based on the analysis and anecdotalevidence it was determined that brush wear was the most criticalailure mode. Other ailure modes such as rotor and stator short

cicuit and open circuit conditions were ound to occur signi cantlyless o ten and were not considered critical ailure modes. Theability to detect these ailure modes was explored and described inthis paper but the ocus o the e ort was to develop a prognostictechnique or brush wear.

Battery FMEA Results

The vented aviation nickel-cadmium (NiCd) helicopter batteryprovides power to the starter-generator or turbine engine starting

and unctions as a back-up emergency source o DC powerfight operations in the event o a DC generator ailure. The oo the battery e ort was to develop an accurate State o(SOC) prediction capability or the NiCd battery. The FMEA

or the battery did not result in comprehensive indications battery ailure modes due to the lack o battery ailure postin ormation. In ormation rom the battery manu actulaboratory test data indicated that the battery health managesystem should be designed to regularly measure accurateduring helicopter operation in order to predict when the bamaintenance procedure should be implemented.

Power Inverter FMEA Results

The power inverter provides emergency backup power to thVAC Essential Bus in the event o a AC generator ailuinverter receives DC power rom the Battery-Generator Buobjective o the power inverter e ort was to develop a diand potentially a predictive capability or the ailure modhave the highest probability or detection. The FMEA resthe inverter only provided a very limited set o ailure data tthe most dominant ailure modes, but based on the interviewanecdotal evidence rom Tobyhanna Army Depot it was deterthat the current source inductor (CSI), bulkhead transistors acircuit card are the dominant ailure modes. It was deterthat extensive modi cation to the circuit card would be reqto conduct diagnostics or this device, so based on engin judgment this was not pursued. The primary ocus o thwas to develop a diagnostic and potentially predictive techn

or the CSI and bulkhead transistors as a lumped componenexternal sensors that were applied to the input and output assemblies. Additionally, the approach developed or the C bulkhead transistors was applied to the output resonant tank (

3. Operational Helicopter Field TestAn operational helicopter eld test was conducted at Fort Rucan OH-58D Kiowa Warrior helicopter as shown in Figure 1 tothe electrical power system signal and noise environment.

Figure 1 Field Test on a OH-58D Kiowa Warrior Helicopter



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The helicopter power system was instrumented with 20 sensorsincluding current, voltage and vibration transducers as well as atachometer or measuring engine speed. Data was gathered at asample rate o 50 kHz per channel and or durations o up to 80seconds. The ocus o the data gathering was to acquire data or twomodes o operation. The rst was to gather data when the power buses were energized and the avionic systems were turned on.During this mode the engine was not running and the battery wasproviding bus power. The second mode consisted o gathering dataduring the engine start period through a ull engine RPM/fat pitchoperating state. This test was conducted multiple times both rom a battery start and a ground power start con guration.

The data that was generated provided in ormation about the powersystem levels and dynamics that were use ul or the technologydevelopment. The data in Figure 2 shows the time sequence andamplitude range or the generator current during the engine startsequence.

Figure 2 Generator Current Data during a Start Procedure

The DC characteristics o this data were used to design the test benchor the generator to emulate the characteristics o the helicopter.The AC characteristics o this data were used to acilitate thedevelopment o the ault detection algorithms. An understanding othe high requency dynamics provided in ormation that was use ul

or the development o current signature analysis ault detectiontechniques. The eld testing also helped determine the e ectivenesso the sensors that potentially will be used or conducting healthmonitoring on the helicopter.

4. Health Management Detection andPrognostic Technology Development

The health management technology development or each powersystem component will be discussed separately by component.

Starter-Generator Health Management Technology

The process or starter-generator diagnostic and prognostic tech-nologies consisted o developing and evaluating models o themachine. This initial modeling allowed or the identi cation opotential observables that can be used to implement predictive

maintenance technologies. In addition, this modeling allothe advance selection o sensors, initial algorithm designestimate o data processing requirements. For the starter-gthe modeling consisted o a magnetic nite element anasimilar (or surrogate) starter-generator since the constructioo the objective machine were not available.

From machine dimensions, coil design and coil con gutime-stepping magnetic nite element analysis was per oeach time step, the FEA yields the magnetic fux vector at evtion in the machine as depicted in Figure 3.

Figure 3 Magnetic FEA Flux Vector Results at a Single Time-Ste

The magnetic fux and the machine state variables at that tiallows calculation o all coil voltages. Once the induce

mechanical motion) coil voltages are determined, the rotordictates the electrical circuit con guration (which is brushdependent). The resulting circuit con guration is then solvstandard circuit analysis techniques. In summary, the FElation and subsequential circuit analysis per ormed at a sion o time steps yields armature voltage, armature currvoltage, eld current, and even quantities that would not beto measure on a physical system such as the current in an incoil. Using the FEA model, it is very straight orward and

ast to per orm the time-stepping analysis or a starter/with various degrees o winding ailures or brush/comde ects.

The FEA simulations were per ormed on the surrogate or normal (baseline) operation as well as a variety o wi brush/commutator ailure modes. Scenarios evaluated inc

Field winding short circuit Armature winding aults Brush resistance increase (uneven wear) Commutator bar short circuit Commutator bar open (bad segment)


continued rom paPOWER SYSTEM PROGNOSTICS FOR THE U.S. ARMY OH-58D HELICOPTER


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Reliability ModelingThe RIAC Guide to Reliability Prediction, Assessment and Estimation

Each o these scenarios was evaluated at various operating speedsand load currents to determine observable quantities that are indic-ative o the ailure mode. From these studies, several observableswere identi ed including: trans er impedance, which is de ned asthe induced (internal) armature voltage per unit o eld current (E0/Ifd), eld current at the pole-passing requency, and the componento the eld current at the commutation requency. A summary o thethree identi ed observables and how they are a ected by various

aults as determined rom the FEA studies is included in Table 1.

Table 1 Summary o Fault E ects on Observables as Determined rom FEA Studies

The model analysis indicated that the trans er impedance providesa detection capability or all six o the ault types. It was also deter-mined that the eld current at the pole-passing requency is also avery use ul parameter or identi ying various armature and com-mutator related aults.

The second step in the starter-generator diagnostic and prognostictechnology development was to conduct laboratory seeded aulttesting on a surrogate DC machine to con rm the simulation results.

The aults seeded into the machine include shorted eld windings,open/short circuit armature windings, open and shorted commu-tator segments, brush sur ace area reduction, and increased brushsparking.

A summary o the three identi ed observables and how they area ected by various aults as determined rom the seeded ault testsis included in Table 2.

Table 2 Summary o Fault E ects on Observables as Determined

rom Seeded Fault Tests

The results o the seeded ault testing indicated that all six o theault types can be detected using the trans er impedance parameter,

that armature and commutator shorts can be detected using eld

current at the pole-passing requency and that the eld curthe commutator requency showed potential or detectingsparking, which is a parameter that is use ul or monitorinwear.

The next step in the starter-generator technology developprocess was to conduct seeded ault tests on the helicopter generator. Since the FMEA determined that brush wear wmost dominant and critical ailure mode, the testing was on the development and validation o a brush wear prognostinique.

To quanti y brush wear, the starter-generator was coupledspindle motor, as shown in Figure 4, and to a variable electric bank.

Figure 4 Prime Mover (le t) and Starter-Generator (right)

The armature load could be varied rom no load through acurrent o 200 A, with speeds ranging rom 7,000 to 13,0The brush wear during the test was determined rom initiend-o -test brush length measurements.

Given the nearly constant brush pressure provided by the springs, the wear rate was ound to be approximately linea

respect to load current and rotational speed. It should also bethat the wear rate increases with sparking at the brush/commtor inter ace. An increase in sparking activity has been shprevious literature [1] to increase wear rate by a wear rate The combination o the wear rate and the wear rate actor an algorithm to determine brush wear rate. The algorithm, sin block diagram orm in Figure 5 utilizes several sensor intrack the brush wear based on observed operating conditionsparking levels.

Figure 5 Brush Wear Prognostic Algorithm

The brush wear tracking algorithm uses speed and armature crom the wear rate algorithm in combination with the ob

sparking index to obtain the wear rate in inches per hour. Theindex is determined rom the component o the eld currencommutation requency. Using previously published visua



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index identi ers [1], the observed brush sparking level and cor-responding eld current at the commutation requency was usedto relate eld current at the commutation requency to a wear rate

actor. That is, eld current measurements and analysis are used todetermine the wear rate actor. The wear rate is integrated to obtainthe cumulative brush wear in inches. Given the initial conditions(new brush length in inches) the remaining brush percentage can bedetermined. As long as the proper initial conditions are established(i.e. when new brushes are installed) the algorithm will provide themaintainer estimates o remaining use ul brush li e as shown inFigure 6.

Figure 6 Brush Wear Trend and Prognostic Indication

This starter-generator display provides a condition indicator andhistorical trend data or brush wear based on a simulated opera-tional usage pro le as well as a RUL prediction in fight operationdays. Data samples as shown by the blue data points are representa-

tive o snapshots taken at helicopter start-up, one snapshot duringeach fight hour and one snapshot at shutdown. The yellow and redhorizontal lines represent the brush wear warning and alert limits,respectively. The red line extending rom the blue data pointsrepresents the predicted data trend. The peach colored con dence bounds that track with the wear prediction provide a orecastingcon dence range or the prediction. Where this orecasted trendcrosses the alert level provides the RUL prediction in fight opera-tion days. The viewing time history o the plot is user selectable between one minute, one hour, one day, one month, six months, oneyear and three years.

Battery Health Management Technology

Based on the FMEA results and an engineering analysis, the ocuso the battery health management technology task was to developaccurate SOC capability that could be implemented on the helicop-ter and gather data during fight operations.

The helicopter battery, MarathonNorco Aerospace model numberSP-170A, has a nameplate capacity o 17 Ah. This can be veri ed ora new, healthy and ully charged (using the manu acturers speci ca-tion) battery by discharging that battery at 17 amps and measuring 17Ah o charge fow be ore the terminal voltage drops to 20 V, which is

the terminal voltage that the manu acturer uses to de ne ema 20 cell sintered plate vented NiCd battery like the SP-170Atimes 1.0 V/cell). In our work we have chosen 19 V as thevoltage using the nearly equivalent convention o (20-1) c1.0 V/cell. The ully charged, ready- or-service condition this nameplate capacity entails charging the battery with asource capable o reaching well over 28 V to nearly 35 V

Most secondary (i.e. rechargeable) batteries do not mainnameplate capacity inde nitely over their li espan. For o our discussions, the term ully-charged capacity withe number o amp-hours a user can obtain rom a battery been ully charged (by some de nition) be ore the battnal voltage alls below 19 V. O ten, this charging is donvoltage and the charging pro le used may or may not ret battery to the nameplate capacity.

Many actors can contribute to a reduction in the amount (measured in Ah) that a user can extract rom a ully charg be ore the terminal voltage is less than 19 V. Factors that

ully-charged capacity rom the nameplate capacity inclcount, depth-o -discharge o previous cycles, temperatudischarge, and charge voltage. These actors impact the unmechanisms that contribute to the reduction o nameplate (electrolyte or electrode chemical or mechanical change, orFor many battery chemistries, the reduction rom nameplaity can be gradual and permanent. However, or the SP-10 battery the nameplate capacity can usually be ully restorlowing a prescribed reconditioning maintenance action thatthe battery more ully than occurs autonomously on the h

ollowed by a restoration o electrolyte levels that may havrom heavy charging. It was the purpose o this project t

system which can determine and predict when this main

action should be per ormed or individual batteries.It will also be use ul or discussion purposes to de ne astate o charge, which is the instantaneous amount othat is le t in a battery, expressed as a percentage o its ncapacity. A measurement o SOC can theoretically be madduring a cycle and can vary rom 0% to greater than 1healthy battery.

A physics based model approach was selected or the battnostic and prognostic technologies based on several actoring: the battery chemistry, battery operational usage pro lecharging scheme on the helicopter. The physics-based m

the battery outlined in Figure 7 is motivated by the Bode scheme [2], [3].




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Figure 7 Block diagram o the Physics-Based Model o the Battery.(Inputs enter the model to the le t o the dotted line. To the right,

wide colored arrows represent the low o charge.)

The model is centered around three buckets that hold chargeQ1, Q2 and Q3 rom which the state o the battery is primarilyderived. Q1 nominally represents the amount o charge storedin the charged beta-NiOOH phase, Q2 nominally represents thecharge stored in the gamma-NiOOH phase (or more accurately, thegamma-[2NiO2NiOOH](KOH2H 2O) phase), and Q3 representsthe amount o lost overcharge that goes into the electrolysis o theelectrolyte. The current and the change in time stamp between twosuccessive iterations is used to calculate, based on its sign, an inputor output incremental charge dQ that either charges or dischargesthe battery. During charge, the Charge Acceptance Model passes acertain raction o the input charge to Q1. That raction is a unctiono Q1, Q2, current and battery temperature. The amount le t over,dQ, is available or Q2 to accept. What remains a ter that, is used

to increment Q3. A conversion actor is then used to convert Q3 intoan equivalent volume o lost electrolyte. During discharge, the Dis-charge Model rst determines what raction o the measured outputcharge dQ is to come rom Q1. What is le tover, dQ, is taken romQ2. The decrement o Q1 is in general larger than the di erence between dQ and dQ because o discharging ine ciencies that area unction o current and temperature. Largely or simplicity, themodel as a whole attributes all charge and discharge ine cienciesthat are unctions o current and temperature to Q1 because it is thedominant source o charge, and we have little experimental basiswith which to split the ine ciencies between Q1 and Q2.

The state-o -charge is calculated in an Available Charge Model rom

the sum o Q1 and only a raction, at present taken to be 16%, o Q2,to represent the sequestered nature o the available charge in thealpha-gamma couple due to the poor mobility o potassium in thenickel electrode. (A better model would include a time dependence

or the di usion o potassium in the lattice.) Corrections are madeor current and battery temperature [4], [5], with the option (usually

taken) o calculating the SOC or a xed output current such as 17 Aas is done by the manu acturers rating method.

Both Q1 and Q2 are assumed to decay slowly in a Charge RetentionModel, exponentially in time, with a time constant that depends

linearly with temperature, the dependence coming rom litevalues [5].

The model was implemented in LabVIEW, a graphical proming language that inter aces to the user through a ront shown in Figure 8.

The inputs to the model are displayed by large indicators at tle t portion o the inter ace and the upper graph shown in8. Outputs are shown by the large indicators at the top righthe lower graph. Small controls and indicators scattered aroupanel control and indicate operation o the model. The panscreen shot during a run on a historical data set o 52 ull disto 19.0 V and subsequent charges at a constant potential oThe model can accept historical data as shown in the gure,ured real time data, or synthetic data as inputs. The stepped trace o the lower graph indicates the amount o charge the actually supplied at discharge. The white trace in the lower shows the models prediction o the SOC, which drops to neat discharge events then rises during the charging events. Wiparameters in the model during this run, the model does a job with its predictions, although it slightly overestimates that early discharge cycles and slightly underestimates SOC adischarge cycles.

Figure 8 User Inter ace to the Model, Showing Results Based on Historical Data

In the upper graph, the green trace is the battery temperaorange is the battery voltage and magenta is the battery cuIn the lower graph the red trace is Q1, blue is Q2, white is thdicted state-o -charge, yellow is the amount o charge accu


continued on next page


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in the input data over the previous charging event, and green is theamount o charge in the input data that le t the battery during theprevious discharge event.

Internally, the model stores various partial derivatives that are used tocalculate changes in Q1 and Q2. Figure 9 shows the change o Q1 withincoming charge dQ during charging, as a unction o Q1 and Q2.

Figure 9 Charge Acceptance Model or Q1, Nominally Representingthe Readily Available Charge Stored in the Beta-Beta Couple o the

Nickel Positive Electrode, as a Function o Q1 and Q2

The shape o these unctions are controlled through additional rontpanels o the model where the user can adjust the various heights,widths, boundaries, etc. that de ne the unctions. In Figure 9, the

height o the plateau determines the charge e ciency with whichQ1 accepts charge. The boundary where the unction goes to zerocontrols the maximum charge that can be held by Q1. This is a ected by Q2. In e ect Q2 is used as a signal or Q1 to accept more chargeon the initial cycles a ter a reconditioning event. To some extent thisis an ad-hoc assumption made to match the long term decay seen inthe datathe memory o how long it has been since reconditioningis stored by the model, e ectively, in Q2.

The charge acceptance o Q1 is also modeled as being infuenced bytemperature and cell voltage, as seen in Figure 10. At low voltages,Q1 keeps most o the incoming charge or itsel . At larger voltagesit allows more incoming charge to pass to Q2. In other words, the

model allows Q2 to only be charged at the relatively high voltagesassociated with reconditioning the battery, thus allowing a reset tothe beginning o the long decay o charge acceptance and SOC asa unction o cycle number. The charge acceptance o Q1 is alsolowered by cold temperatures and especially by high temperatures.From literature values, the NiCd battery holds less charge at hightemperatures [6], [7].

Figure 10 Charge Acceptance Model or Q1, Nominally Represing Available Charge Stored in the Beta-Beta Couple o the NickPositive Electrode, as a Function o Battery Temperature and C

VoltageWhatever charge, dQ, not accepted by Q1 is available o

raction that is accepted is shown in Figure 11.

Figure 11 Charge Acceptance Model or Q2 as a Function o Qand Cell Voltage, Nominally Representing the Sluggish Charge

Stored in the Alpha-Gamma Couple. (Whatever charge that is no

accepted here is assumed to electrolyze water.)

The unction shown in Figure 11 is limited as a unctioexpress the nite charge holding ability o the electrodes. tion drops as a unction o voltage to allow more chargto Q3 and electrolyze the aqueous electrolyte. A conversio 0.245 cc/Ah, given by the battery manu acturer [4], estimate the volume o electrolyte lost rom the accumuThis actor di ers rom the theoretical stoichiometric vacc/Ah or reasons we do not yet appreciate. Perhaps the drepresents charge loss due to mechanisms other than electro




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The Discharge Model is simpler than the Charge Acceptance Modelas shown in Figure 12.

Figure 12 Discharge Model or Q1, as a Function o Q1 and Q2

The graph shows that or most values o Q1 and Q2, most o thecharge leaving the battery is assumed to come rom Q1. This is howthe model allows Q2 to decay slowly (motivated by the slow di u-sion o potassium in the alpha-gamma phase) and account or theslow decay o charge acceptance and SOC.

Only i Q1 is empty, i.e. near zero, and, nevertheless, charge is meas-ured to be leaving the battery, is sizable charge taken rom Q2. IQ2 is empty all the charge comes rom Q1, although Q1 is neverallowed to become negative (thus in e ect resetting the model isomehow not enough charge is in Q1 and Q2 to account or the

amount o charge measured to be leaving the battery).The Discharge Model takes more charge rom Q1 than is measuredleaving the battery at extreme currents and temperatures, as shownin Figure 13. This unction is constant as a unction o current at lowcurrents and increases logarithmically with current at higher currentas described in the literature [5], [4]. Its temperature dependenceis a spline t to literature data modi ed to account or our limiteddata on these particular batteries [5], [6]. The same current andtemperature correction actors are used in the estimate o SOC inthe Available Charge Model o Figure 7.

Figure 13 Discharge Model or Q1, as a Function o Temperatureand Current

Lastly, the decrement o Q2 per whatever charge dQ that Q1take responsibility or is unity unless Q2 is running out oas seen in Figure 14.

Figure 14 Discharge Model or Q2, as a Function o Q2

The zeroing o the derivative at small Q2 is another mechanwhich the model resets itsel rom long term dri t. It takes ao the knowledge that Q1 and Q2 cannot both be negative

the same time be supplying current out the battery terminalsmodel so ar makes little use o the battery voltage in ormmay be possible in a more advanced model to use such in orto help the model correct itsel rom long term dri t in the aboccasional ull discharge.

The ocus o this program was to provide the Army wability to maintain aviation NiCd helicopter batteries (specthe SP-170A) using an on-condition approach. Compared on-schedule approach which is currently utilized, this on-conapproach is expected to allow a longer time between mainte




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periods which will reduce maintenance costs and increase up-timeo the helicopter. The physics based battery model provides themaintainer with the ability to track the available charge/SOC inamp-hours and predict the remaining use ul li e o the battery asshown in Figure 15.

Figure 15 Historical Trend Data and RUL Prediction or theBattery

This battery display provides condition indicator historical trenddata or electrolyte level and available charge based on a simulatedoperational usage pro le as well as a RUL prediction in fightoperation days. Data samples as shown by the blue data points arerepresentative o single reports based on continuous measuremento battery voltage and current to accumulate charge/dischargecurrent snapshots taken at helicopter start-up, one snapshot duringeach fight hour and one snapshot at shutdown. The yellow and redhorizontal lines represent the electrolyte level and available charge

warning and alert limits, respectively. The red line extending romthe blue data points represents the predicted slope or the datatrend. The peach colored con dence bounds that track with theprediction trend provide a orecasting range or the prediction.Where the orecasted trend or either the electrolyte level or availablecharge crosses the alert level provides the RUL prediction in fightoperation days.

Inverter Health Management Technology

The inverter health monitoring technology is based on a modelbased approach. Real-time measurements o the observed inverteroperating parameters are compared to those predicted by a circuit

model o the inverter. Deviations between observed and predicted behavior provide indicators o aults in the critical componentsidenti ed in the FMEA.

The rst step in developing the model o the inverter involvedidenti ying the topology used in the OH-58 inverter; di erentinverter topologies use di erent con gurations o components andwill there ore have di erent circuit models. A ter identi ying theinverter topology, we identi ed the speci cations or and opera-tional characteristics o the actual inverter components (both thecomponents o interest rom a health monitoring standpoint and

the components required to build a circuit model o the iComponent values were identi ed through direct measuin erred rom measured per ormance characteristics, an

rom comparison o simulated per ormance (with candiponent values) to measured characteristics.

A circuit simulation model was developed to compare thcharacteristics o the parameterized inverter model to thmodels. The model was developed in Multisim and at ttime the circuit was parameterized in order to con rm oucalculations. Development o the circuit model required seassumptions. The output resonant tank (ORT) was treateideal voltage source. The model was then developed worktoward the input voltage source until the current source in(CSI) and MOSFETs, and the RMS to DC converters gaoutputs. At this point the ideal AC source was replaced bresonant tank circuit. The Pulse-Width Modulation (PWMMOSFETs was simulated as a circuit input. The datasheePWM IC was used to build the PWM IC and placed into thso that there was automatic voltage regulation [8].

The circuit models that were developed or this program wto identi y aults in the inverter components. Measured por per ormance were compared to the model componentspredicted per ormance to identi y aults in inverter sectioponents.

Looking into the inverter rom the output reveals two basithe rst when the MOSFETs are on and the second wMOSFETs are o . Knowing that the inverter operates i

erent modes and that they happen at regular intervals, thevoltage and current signals can be separated based on the mode. Both topologies need to be separated urther to

or initial conditions. This makes sense or the MOSFETwhere there are two well de ned switches changing the pothe current rom the CSI that may have a non-zero initial c

The reasoning or keeping track o which MOSFET OFinverter is in is less apparent, because the topology is the both o these segments. I the circuit equations includes there is most likely a constant that represents the initial conthe integral. This initial condition alternates signs thus rthe knowledge o the operational mode o the MOSFET.

The structure o the detection algorithm is to determine thetopology mode o operation, generate the needed derivat

integrals, per orm coe cient estimation using Batch LeaEstimation (BLSE), and scale the output or re erence asFigure 16.

Figure 16 Coe icient Estimation Block Diagram




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In order to develop an algorithm to segment the signals so they can be used in coe cient estimation routines, a well de ned under-standing o the operation o the inverter and the expected signals isnecessary. It is possible to use either the output voltage or current,although the current tends to be dependent on the load, while thevoltage tends to depend more on the supply voltage and not theload.

A quick inspection o the output voltage wave orm as shown in thetop plot in Figure 17, does not show any signs o when switchingoccurs, but by looking at the rst derivative o the output voltageas shown in the middle plot o Figure 17, the switching events areobservable. These transitions are even more noticeable as a peak inthe second derivative o the output voltage as shown in the bottomplot o Figure 17.

Figure 17 KGS Inverter Output Voltage, First Derivative and

Second DerivativeTo automate the process o nding the topology transitions, analgorithm was developed to identi y these peaks in an accurate andorderly ashion. This is done in a three-step process: determine theoperating requency, lter the signal, nd the turn-o and turn-ontransitions. The detection algorithm is designed to work on onesecond o data but is capable o handling longer or shorter lengthso data.

The requency o the voltage is needed to calculate the samples percycle used in both the turn-o and turn-on event detection algo-rithms. These algorithms estimate the location o peaks over the

entire length o the sampled signal, and require accurate voltagemeasurements to accomplish this process. The accuracy needed can be accomplished by taking a Fast Fourier Trans orm (FFT) o theoutput voltage. Zero padding to the next larger power o two isadvised or FFT speed, but is not necessary to increase accuracy.

The second derivative o the output voltage is run through a highpass lter with a cut-o requency set to 2.5 times the measuredoutput requency. This cut-o requency was set to maximizethe attenuation o the undamental (400 Hz in this case) withoutdegrading the peak amplitudes.

The process starts by identi ying the turn-o transition it produces a larger spike on the second derivative o the voltage as shown in Figure 18. The larger relative size o this due to the CSI having more current when the MOSFETs tthan when they turn on. A single cycle o data is used to idenlargest positive and negative peak.

Figure 18 Initial Turn-O Peak Detection

This is where the requency accuracy is needed and is notiAn FFT with su cient zero padding or a nominal 400-Hsampled at 50 kHz (about 125 samples per cycle), could rea 3 to 4 sample dri t over 50,000 samples and cause inaidenti cation o turn-on and turn-o times. Using the relathat these peaks occur once every cycle at one cycle intervaexpected peak location is projected over the entire one secdata. Because the expected peak location is only accurate

sample, the interval around the expected peak is searched local positive or negative peak, and the peak location is upda

Since the turn-o peaks are larger than the turn-on peaks, tho peaks and the area around them are zeroed out a ter tidenti ed so the largest remaining peaks are the turn-on peshown in Figure 19.

Figure 19 Initial Turn-O Peak Detection

http://theRIAC.org 17continued on next page


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Once the MOSFET turn-o transitions have been zeroed out the peakidenti cation algorithm is applied again to identi y the MOSFETturn-on transitions. The nal results o the topology transitionlocation algorithm provides a map o the MOSFET states as shownin Figure 20 that are used or the ault detection procedure.

Figure 20 Inverter Operational Topology Segments

In order to track changes in the inverter a method o Batch Least-Squared Estimation is used. The method accepts inputs o measuredsignals, their derivatives as well as integrals and then outputsthe parameter estimates [9]. These parameters estimates are thecoe cients ound in ront o each o the derivatives and integralsin the mathematical equation describing the MOSFET ON andMOSFET OFF models. When the model is generated it lumps valuestogether such as the MOSFET drain to source-on resistance and theCSI resistance into the CSI resistance, consequently reducing the

ability to detect changes in the MOSFET drain to source resistance.The in ormation in Table 3 below provides an indication o thedetectability o each ailure mode using coe cient estimation.

Table 3 Component Failure Detection Probability using Parameter

Estimation

A higher order BLSE model shown in Figure 21 was used or theMOSFET ON topology to reduce loading changing e ects. Modelswith only capacitors and inductors, and without leakage inductanceand primary winding resistance were tested but showed noticeablechange when loads were changed.

C_tL_t

L_csi

V_busZ

Z = A + j B

XCP1

XSC1

A B

Ext Trig+

+

_

_ + _

L_lR_llR_csi

R_c

Figure 21 BLSE Model and Component Table or MOSFET OTopology

A simpli ed model o the output as shown in Figure developed. It consists o an inductor capacitor resonant the load. This model worked well at the load level being tthe long term test bed, but showed load disturbances. Thiwas kept simple because it was observed that the invertemost o its time in the MOSFETs ON topology.

C_tankL_tank Z_load

Z = A + j B

XCP1

A B

Ext Trig+

+

_

_ + _

Figure 22 BLSE Model or MOSFET OFF Topology

Seeded ault testing was conducted and one second worthrom the inverter was recorded every ten minutes. Each

one second samples is considered an index in time. Faload changes were implemented instantaneously, which rin step changes in the parameter estimation. Three seedewere induced to validate the detection capability o the pestimation algorithm. The aults induced in the inverter

those listed in Table 4.

Table 4 Inverter Seeded Fault Indexes




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Faults seeded in the ORT, output capacitance, should show up in both the MOSFETs ON and MOSFETs OFF parameter estimations, but aults in the CSI, CSI inductance and resistance, should onlyshow up in the MOSFETs ON parameter estimation. At the sametime load changes should not show up in the parameters estima-tions, but are noticeable in the MOSFETs OFF estimation as indi-cated in Figure 23 at index 1969.

The results or the MOSFETs OFF case correctly identi ed the capac-itance change that occurred at index 6298 in Figure 23 as an increaseo capacitance, but also identi ed other capacitance and inductancechanges that did not occur. The MOSFET OFF parameter estimationpicked up cross talk between phases at index 1969 when a change incapacitance in phase A was also indicated in phase B as an increasein capacitance. The spike that occurs on phase B at 11034 to 11185is the result o installing a damaged CSI on that phase; once againthis should not show up on the MOSFET OFF parameter estima-tion, but is a result o a low component count model being used

or parameter estimation. This in itsel does not constitute an issue because the inverter would have to get replaced, but the parameterestimation also indicated a capacitance change at index 1969 thatwas a load change. The change in load e ect is more noticeablewhen the load current is varied and the parameters are estimatedas shown in Figure 23. The result o this data analysis does notindicate that a correction curve should be used, but instead that ahigher component count mathematical model should be used.

Parameter estimation or the MOSFET OFF case was done usinga two component model, which allowed parameter estimation oactual circuit parameters. The MOSFET ON case used 10 compo-nents which resulted in the estimated parameters to be made up ocombinations o the actual circuit parameters. The model equationused or the MOSFETs ON estimation uses a more complete model,

and as a result does not show the loading e ect that the MOSFETsOFF estimation su ered.

Figure 23 MOSFETs OFF Two Component Parameter Estimation

Though the intent o this program was to potentially deveinverter prognostic capability, this was not achievable due to ity to achieve results rom the accelerated li e testing in timpaper.

5. Health Management Hardware andInterface Implementation

The power system health management technology includes sethree component health nodes, one power system health hub ainter ace display as shown in Figure 24.

Figure 24 Power System Health Management Hardware BlockDiagram

The nodes and health hub are comprised o a PC-104 conetwork (small ormat computer running Windows XP) Windows-based graphical user inter ace (GUI) was devto display the results rom the development o the management technology team in a single, cohesive environThe majority o the data processing occurs at the node levelprovides data acquisition capability and condition indicationalgorithm processing capability. The power system health representative o the HUMS system and provides the systemhealth assessment and display processing capability. The system health hub is an Intel Core 2 Duo based computer swith an Intel Graphics Media Accelerator 4500MHD to supp

3D animation graphics in the user inter ace. The data can pot be downloaded to a ground station rom the power system hub or use by maintenance personnel. The graphical user inprovides an in ormational data link between the three subsdata collection/processing nodes (battery, starter/generator inverter) and the power system health hub.

The intent o the hardware/so tware architecture was to pertinent condition based maintenance condition indicator

unctional parameters rom each o these subsystem nopresent it in an e cient and user riendly manner or t




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technology demonstration as shown in high level block diagram othe so tware architecture in Figure 25.

Figure 25 So tware Architecture Design o the Power System Health Monitor User Inter ace

The so tware architecture layout consists o a main overviewscreen display that leads to component level overview screens andhistorical as well as trend plotting or each component. All o thedisplays are setup and initialized through a con guration screen.

The graphical user inter ace is a Windows based application meantto provide the user with an intuitive inter ace that will providediagnostic and prognostics predictions or the helicopter powersystem components. A three dimensional (3-D) helicopter model isdisplayed on the main screen o the helicopter power system healthmonitor application as shown in Figure 26.

Figure 26 The Main Screen o the Helicopter Monitor UserInter ace

The purpose o the main screen is to display high level status andault isolation in ormation or the power system components. The

le t portion o the display provides status icons or each component.The right portion o the display provides a 3-D version o a OH-58DKiowa Warrior model that provides a intuitive capability orproviding ault indication and isolation in ormation.

The ault in ormation is indicated using simple green, yellow, redtype symbolic indicators or each component based on its current

health. This provides a visual aid to the user on the exacto the ailing component, which can aid in the troubleshoreplacement o the components. The bottom portion o thprovides text based alerts rom the health monitor.

6. Conclusions

The helicopter power system health management technodesigned to be embedded on the helicopter and integratethe HUMS. The objective o the technology implementaprovide diagnostic and prognostic condition indicators ito lengthen the maintenance interval which will help to operational availability and reduce sustainment costs.

This e ort ocused on development o prognostic conditor algorithms or the detection o power system aultsthe starter-generator, battery and inverter. Hardware, so twin ormation inter aces were developed or the technolomentation to show the utility o the technology or powhealth monitoring.

Acknowledgment

This work was supported jointly by the Aviation Applied Togy Directorate (AATD) and Bell Helicopter through a UOperations Support and Sustainment Technology (OSST) The content o the in ormation does not necessarily refection or policy o the Government or Bell Helicopter, and nendorsement should be in erred.

The views and conclusions contained in this document ao the authors and should not be interpreted as represen

o cial policies, either expressed or implied o the AviatioTechnology Directorate or the United States Government.

References[1] R.J. Hamilton, DC Motor Brush Li e, 1998 IEEE Indus

tions Con erence, vol. 3, pp. 2217-2224.[2] A. H. Zimmerman, Nickel-Hydrogen Batteries Principle and

El Segundo, CA: The Aerospace Press, 2009, pp. 87107.[3] D. Berndt, Maintenance-Free Batteries: Based on aqueous

3rd ed. Baldock, Hert ordshire, England: Research Studies 2003, pp. 208214.

[4] MarathonNorco Aerospace, Operating and Maintenance MNickel-Cadmium Aircra t Batteries. Waco, TX, 1997.[5] J. M. Evjen and A. J. Catotti, Vented sintered-plate nicke

batteries, in Handbook o Batteries, 2nd ed., D. Linden, Ed. McGraw-Hill, 1995.

[6] General Electric Company, Battery Products Section, NickelBattery Application Engineering Handbook. Gainesville, FL,

[7] Varta Batterie AG, Sealed Nickel Cadmium Batteries. DusseVerlag GmbH, 1982.

[8] http:// ocus.ti.com/lit/ds/symlink/sg2524.pd[9] strm, Karl J., and Wittenmark, Bjrn, Adaptive Control, 2nd edit

Dover Publications, Inc., 2008, pp 41-49.




21/36http://theRIAC.org 21


22/36

A SHORT HISTORY OF RELIABILITY

A REPERTOIRE demo is available at:http://theRIAC.org

REPERTOIRE is the RIACs set o interactive reliability engineeringtraining courses developed around the American Society orQuality (ASQ) body o knowledge or the Certifed ReliabilityEngineers (CRE) exam. Whether you are preparing or the CREexam, or just need some basic training or re reshing in reliability,youll appreciate the convenience o training at your own pace,on your own schedule.

The combined set o ive courses contains approximatelythirty hours of narrated training , with around six hours o content in eachcourse.

The available courses cover:

Reliability Management (REPER-01)

Probability and Statistics or Reliability (REPER-02)Reliability in Design and Development (REPER-03)

Reliability Modeling and Prediction (REPER-04)

Reliability Testing (REPER-05)

Each o the fve courses is divided into independent modules thattypically take about one hour each to complete.

The web-access version o REPERTOIREcontains hundreds o quizquestions and interactive exercises (about 10-20 rein orcement ques-tions per module), so students can assess their progress and reviewthose areas where they may need improvement. The questions are

ORDER CODE : US PRICE NON-US WEB ACCESS

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RE PERTOIRERELIABILITY TRAINING

FOR PROFESSIONALS ON THE GO

automatically graded andstored byREPERTOIRE or

uture re erence.(Note thatthe quizzes and interactiveexercises are not included inthe DVD version ).

Purchase o the entire fve-course set on DVD ( REPER-DVD ) or via web access ( REPER-FULL ) includes a copy o the Quanterion Solutions Inc. QuARTPRO so tware set o automated reliability tools.

View the online demo at: http://theRIAC.org

THE JOURNAL OF THE RELIABILITY INFORMATION ANALYSIS CENTER // JANUARY 2012

Paul EngelhartRIAC Contracting O cers Representative,

Air Force Research Laboratory

Joseph HazeltineRIAC Director,

Technical Area Task (TAT) Manager

Preston MacDiarmidRIAC Technical Director

Valerie HayesRIAC Deputy Director TATs/SAs

David NichollsRIAC Operations Manager

David MaharSo tware & Database Manager

Patricia SmalleyRIAC Training Coordinator

P [email protected] .mil

P 256.716.4390 // FAX 256.721.01 joseph.hazel tine@w yle.com

Toll Free 877.808.0097P 315.732.0097 // FAX [email protected]

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The Rel iab ili ty In ormat ion Ana lys is Cen ter100 Seymour RoadSuite C101Utica, NY 13502-1311 Toll Free : 8 77 .36 3.R IAC (74 22)P 315.351.4200 // FAX [email protected]://theRIAC.org

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The Journal o the Reli abil ity In o r-mation Analysis Center is publishedquarterly by the Reliability In orma-tion Analysis Center (RIAC). The RIACis a DoD In ormation Analysis Center(IAC) sponsored by the De ense Tech-nical In ormation Center (DTIC) and

operated by a team led by Wyle Labo-ratories, and including QuanterionSolutions Incorporated, the Center orRisk and Reliability at the University o Maryland, the Penn State UniversityApplied Research Lab (ARL) and theState University o New York Instituteo Technology (SUNYIT).

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23/36

NEW MILI TARY HANDBOOK 189C, RELIAB ILIT Y GROW THMANAGEMENT

Lisa I. Carroll, AMSAA

SummaryThe U.S. Army Materiel Systems Analysis Activity (AMSAA)published the new MIL-HDBK-189C, Reliability Growth Manage-ment, which is critical or implementing the new O ce o the Sec-retary o De ense (OSD) and Army reliability policies.

BackgroundReliability growth management procedures have been developedto improve the reliability o Department o De ense (DoD) weaponsystems. Reliability growth techniques enable acquisition person-nel to plan, evaluate and control the reliability o a system duringits development stage. The reliability growth concepts and meth-

odologies have evolved over the last ew decades by actual appli-cations to military systems. Through these applications, reliabilitygrowth management technology has been developed to the pointwhere considerable payo s in system reliability improvement andcost reduction can be achieved.

Whats NewReliability growth encompasses 3 areas: planning (prior to test data),tracking (using test data), and projection (using test data and apply-ing x e ectiveness actors). Thirty years o lessons learned has cul-minated in the recent development o several models in each o these

areas. Collectively these reliability growth models are re erred to asthe AMSAA Visual Growth Suite and are available ree o charge toUS government personnel and their supporting contractors.

One o the most signi cant models to note is the Planning ModelBased on Projection Methodology (PM2). It develops a system-levelreliability growth planning curve that incorporates the develop-mental test schedule and corrective action strategy. Bene ts includerisk reduction, construction o easible reliability test programs, and bridging the gap between engineering e orts and program con-straints with the overall reliability program.

ConclusionAMSAA published MIL-HDBK-189C, Reliability Growtagement, in June 2011 to refect recent development o relgrowth concepts and methodologies based on applications totary systems. Comments rom Reliability Subject Matter within the Army, Navy and Air Force were incorporated.updated handbook supports new OSD and Army reliability poand was posted to the Acquisition Streamlining and Standation In ormation System (ASSIST) database or use by all o

Biography

Lisa Carroll is a member o the Reliability Analysis Team atArmy Materiel Systems Analysis Activity. She earned heelors degree in Mathematics at Albright College in Pennsyand her masters degree in Statistics at the University o De

Lisa I. CarrollOperations Research Analyst, AMSAAATTN: AMSRD-AMS-LR392 Hopkins RoadAPG, MD 21005-5071USA



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Achieving System Reliability Growth Through Robust Design and Test

Historically, the reliability growth process has been tho

2012 journal riac web_2

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