A Study on Remaining Useful Life Prediction for Prognostic Applic

8/3/2019 A Study on Remaining Useful Life Prediction for Prognostic Applic

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University of New Orleans

ScholarWorks@UNO

University of New Orleans Theses andDissertations

Dissertations and Theses

8-4-2011

A Study on Remaining Useful Life Prediction forPrognostic Applications

Gang LiuUniversity of New Orleans, [email protected]

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Recommended CitationLiu, Gang, "A Study on Remaining Useful Life Prediction for Prognostic Applications" (2011). University of New Orleans Theses andDissertations. Paper 456.http://scholarworks.uno.edu/td/456
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AStudyonRemainingUsefulLifePredictionforPrognosticApplications

AThesis

SubmittedtotheGraduateFacultyofthe

UniversityofNewOrleans

inpartialfulfillmentofthe

requirementsforthedegreeof

MasterofScience

In

Engineering

ElectricalEngineering

by

GangLiu

M.S.DonghuaUniversity,2004

M.S.UniversityofNewOrleans,2011

August,2011


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Copyright2011,GangLiu


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iii

AcknowledgementFirst,Iwouldliketothankmythesisadvisor,Dr.HuiminChen,forhispatienceand

mentoring. Hedevotedhimselffullyintoacademicteachingandresearch.Heismorethan

willingtohelpmeoutevenathisbusiestperiod. NomatterhowlateIsendmywritingtohim,

healwaysrepliesinaverypromptmanner.Hecultivatedmytechnicalwritingskillsandledme

throughtheacademicresearchworld.Hehiredmeasresearchassistantandintroducedmeto

theresearchcommunity,suchasPrognosticCenterofExcellenceatNASAAmesResearch

CenterandPHMsociety.Thankyouforsteeringmeintherightdirectionandthankyoufor

yourhelpinpasttwoyears.

Thankyou,Dr.X.RongLi,Dr.VesselinPJilkov,forservingthethesiscommittee,

teachingmestatisticscourses,hostingtheISLseminarandgraduateseminar.Youguidedme

therightwayandbestwayofgeneralresearchandprovidedmevaluablecommentstomy

works.

Iwanttoexpressmylovetomywife,XiaolanZhou,whodidmorethanhershare

aroundthehouseasIsatatthecomputer.Withouthersupportandgentlepushing,Iwouldstill

behangingaroundmyhometown.Ialsowanttothankmyparents.Withouttheirpatience,

understanding,support,thecompletionofthisworkwouldnothavebeenpossible.

IwanttoexpressmythanktoDr.KaiGoebel,Dr.SahaBhaskar,Dr.SaxenaAbhinavand

otherresearchersatPrognosticCenterofExcellenceinNASAAmesResearchCenter.Duringthe

sitevisit,theygavemeatouroftheirtestplatformsandsharedtheirthoughtsintheprognostic

field.Dr.KaireviewedmyPHMdatachallengecompetitionalgorithmandprovidedvaluable

suggestions.

Iamgratefultothefundingagencies:LouisianaBoardofRegents(LINKSwithIndustry,

ResearchCenters,andNationalLabs LINK)andNASA(EPSCoRDART2).Ialsowanttothank

theGraduateSchoolofUniversityofNewOrleans,andtheOfficeofResearchandSponsored

Programsfor

providing

the

supplemental

fund

through

the

masters

Thesis

Improvement

Grant

(TIG),whichsupportedthedevelopmentofUNObatterytestplatform.


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TableofContents

ListofFigures.................................................................................................................................. vi

Abstract.......................................................................................................................................... vii

1 Introduction............................................................................................................................. 1

1.1 Motivation........................................................................................................................ 1

1.2 PrognosticsandHealthManagement.............................................................................. 2

1.2.1 DataDrivenPrognostics........................................................................................... 3

1.2.2 Modelbasedprognostics......................................................................................... 3

1.2.3 Hybridapproaches.................................................................................................... 3

1.3 ApplicationFields............................................................................................................. 3

1.3.1 LithiumionBatteries................................................................................................ 4

1.3.2 MillingMachineCutters............................................................................................ 5

1.4 PreviousResearches......................................................................................................... 5

1.4.1 EarlyBatteryResearches.......................................................................................... 5

1.4.2 NASA AmesResearchCenter................................................................................... 6

1.4.3 ResearchesforMillingMachineCutterPrediction...................................................6

1.5 Prognosticsapplicationenvironmentandsetup.............................................................6

1.5.1 NASAAmesResearchCenterBatteryTestbed.........................................................6

1.5.2 2010PHMDataChallengeCompetition................................................................... 7

2 ProblemDefinition.................................................................................................................. 7

2.1 GeneralPrognosticProblemFormulation....................................................................... 7

2.2 LithiumIonBatteryModel............................................................................................... 8

2.3 LithiumIonBatteryKeyParameters................................................................................ 8

2.3.1 OpenCircuitVoltage(OCV)....................................................................................... 8

2.3.2 ChargeandDischargeCapacity.................................................................................8

2.4 MillingMachineCutterKeyParameters.......................................................................... 9

3 ApplyLinearRegressiontoPrognostic.................................................................................... 9

3.1 UseEstimatedRemainingCapacityastheRegressor......................................................9

3.2 RegressiontoEstimateEndofChargeTime................................................................. 10


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3.3 RegressionResultonNASAbatterydata....................................................................... 11

4 UNOBatteryTestPlatform.................................................................................................... 13

4.1 Motivationofbuildingthetestplatform....................................................................... 13

4.2 Testcapabilities.............................................................................................................. 13

4.3 TestSetup....................................................................................................................... 14

4.3.1 VoltageControlCurrentServo................................................................................ 14

4.3.2 ConstantCurrentandConstantVoltageChargerCircuit........................................15

4.3.3 ImpedanceMeasurementDuringDischargeCycle................................................15

4.4 TestProcedures.............................................................................................................. 16

5 ConclusionsandFutureWork............................................................................................... 16

5.1 Conclusions..................................................................................................................... 16

5.2 FutureWork................................................................................................................... 17

Appendices.................................................................................................................................... 20

A1. NASATestBedCircuit.................................................................................................... 20

A2. TestBatterySpecification.............................................................................................. 20

VITA............................................................................................................................................... 22


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ListofFiguresFigure1 BatteryEnergyDensityComparison.............................................................................. 4

Figure2 CuttingToolCoordinates................................................................................................ 7

Figure3 SimplifiedBatteryModel................................................................................................ 8

Figure4 BatteryDischargePlot.................................................................................................. 10

Figure5 PredictiononSyntheticData........................................................................................ 11

Figure6 BatteryB0005PredictionResult................................................................................... 11

Figure7 RULerrorat2000sectoend........................................................................................ 12

Figure8 RULerrorat500sectoend.......................................................................................... 12

Figure9 RULerrorat100secondstoend.................................................................................. 13

Figure10 UNOBatteryTestPlatform......................................................................................... 14

Figure11 VoltageControlCurrentServo.................................................................................... 15

Figure12

Constant

Current

and

Constant

Voltage

Charger

Circuit

...........................................

15

Figure13 NASAAmesPCoEBatteryTestbed............................................................................. 20


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AbstractWeconsiderthepredictionalgorithmandperformanceevaluationforprognosticsand

healthmanagement(PHM)problems,especiallythepredictionofremainingusefullife(RUL)for

themillingmachinecutterandlithiumionbattery.Wemodeledbatteryasavoltagesourceand

internalresisters.Byanalyzingvoltagechangetrendduringdischarge,wemadetheprediction

ofbatteryremaindischargetimeinonedischargecycle.Byanalyzinginternalresistancechange

trendduringmultiplecycles,wewereabletopredictthebatteryremainingusefultimeduring

itslifetime.WeshowedthatthebatteryrestprofileiscorrelatedwiththeRUL.Numerical

resultsusingtherealisticbatteryagingdatafromNASAprognosticsdatarepositoryyielded

satisfactoryperformanceforbatteryprognosisasmeasuredbycertainperformancemetrics.

Webuiltabatterytestplatformandsimulatedmoreusagepatternandverifiedtheprediction

algorithm.Prognosticperformancemetricswereusedtocomparedifferentalgorithms.

PrognosticsandHealthManagement,RemainingUsefulLife,LithiumionBattery,Linear

Regression


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1 IntroductionWestudyprognosticandhealthmanagementproblemsrelatedtoremainingusefullife

predictionofmissioncriticalcomponentssuchasalithiumionbatterystateofchargeandstate

oflifeandmillingmachinecutterusablecycles.Weimplementedlinearregressionmethods

andevaluatedthepredictionperformance.Abatterytestplatformissetuptoenablefurther

prognosticstudies.Wethinkneitherpurelydatadrivennorpurelymodelbasedapproaches

wouldworkwellinreality.Atmostcaseswhennogroundtruthisavailableandlackofrunto

failuredata,withthemodelknowledgeandselectivelyprocesscollecteddata,thehybrid

approachworksbest,asfarasthepredictionaccuracyandspeedareconcerned.

Chapter1introducesthemotivationofthework,thebackgroundofprognosticresearch

statusand

typical

approaches,

brief

lithium

ion

battery

and

milling

machine

cutter

knowledge,

previousresearchesandcommercialvalueoftheprognosticproblems.

Chapter2formulatesgeneralprognosticproblemanddefinethekeyapplication

parameters.Lithiumionbatteryexternalandinternalkeyindicesaredescribedandmilling

machinecutterfeaturesarelisted.

InChapter3,wemodeledlithiumionbatteryopencircuitvoltage(OCV)andevaluated

themodelfittingresidues.Batterystateofcharge(SOC)andstateofhealth(SOH)estimation

andpredictionaregiven.Linearregressionisusedtomakepredictionwithconfidenceinterval.

Chapter4describesthenewbatterytestplatforminUniversityofNewOrleans.Ithas

theglobaldiagramandkeycircuitdesigns.

Conclusionsandfutureworkarepresentedinchapter5.Itincludesthefindingsof

batterymodelandapplicationlessonlearnregardinglinearregressionmethod.Thenewtest

platformcanbefurtherdevelopedandasateachingresourceforUniversityofNewOrleans.

1.1 MotivationMyresearchinterestforprognosticsstartsfromthedailyuseofbattery.Inmanybattery

applications,how

to

detect

good

batteries

from

bad

ones

is

always

ahot

topic.

For

example

someAAbatteriesonaWalkmancanlastfortensofhoursbutsomearelessthanhalfhour.

Somebatteriesaregoodforremotecontrollerbutnotforflashlight.ToevaluateAAbattery

remainingenergy,someusesopencircuitvoltagewhilesomeusesshortcircuitcurrent.People

haveexperiencesthatlaptopmeterworksbetterfornewbatteries.Whenbatterygettingold,

orwhenusinglaptopinoutdoorcoldtemperature,laptopmaydielongbeforethepredicted

time.AlltheseexperiencesdroveourcuriosityhigherandhigherandsoIchoosetoresearchon

statisticalfaultprognostics.


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Prognosticisveryimportanttoindustrialusagenowadays.Growingdemandfor

improvingthereliabilityandsurvivabilityofsafetycriticalaerospacesystemshasledtothe

developmentof

PHM.

Instead

of

passively

react

to

fault

symptoms

with

limited

fault

tolerant

capability,aPHMsystemshouldpredictfailuresactivelyandreconfigurecontrolactionssothat

stabilityandacceptableperformanceoftheentiresystemcanbemaintained.Theemergence

andsuccessfulapplicationsofPHMtechnologyoverthelastdecade,especiallythe

developmentofonlineprognosistechniques,gaverisetoproactivefaulttolerancecontrol

system,whichisplayingmoreandmoreimportantroleindeepspaceexploringandmilitary

weapons.Asalltechnologydevelopmentwouldeventuallyservepublicinciviliandevices,so

theprognosticresearchhasgreatcommercialvalueaswell.

InInformationandSystemLab(ISL),ElectricalEngineeringdepartmentofUniversityof

NewOrleans,

we

use

statistical

inference

methods

on

target

tracking

and

signal

estimation.

Statisticalfaultprognosticisnaturallybecomeourinterest.In2009,LouisianaBoardofRegents

setuptheNASAEPSCoRDART2funding,forLouisianauniversitiestoapproachindustrial

groups.In2010programLINKlinksUNOwithindustry,researchcenters,andnationallabs.

Alsothisfieldisdifferentfromtraditionalreliabilityfieldanditisunderdeveloping.The

mathematicalfoundationisnotyetsolid.Henceitwasagreatopportunitywegotintothe

prognosticresearchfield.

ThePHMsocietyorganizesadatachallengesessionannually,whichprovidesanarenafor

allprognostic

algorithms

to

compete

in

real

applications.

In

year

2010

the

data

challenge

focusedonRULestimationforcuttersfromahighspeedComputerNumericalControl(CNC)

millingmachineusingdynamometer,accelerometer,andacousticemissiondata.Both

professionalandstudentparticipantsneededtosubmitestimatedmaximumsafecutcyclesfor

givenwear.IjoinedthedatachallengecompetitionandIwon1stprizeinstudentcategory.It

gavemegreatconfidencethatinUniversityofNewOrleanscandoverywellintheprognostic

researchfield.

1.2 PrognosticsandHealthManagementThere

is

no

agreed

definition

of

PHM

yet.

On

Wikipedia,

the

definition

is

Prognostics

is

anengineeringdisciplinefocusedonpredictingthetimeatwhichacomponentwillnolonger

performaparticularfunction.Thepredictedtimeisdefinedastheremainingusefullife(RUL).

Thescienceofprognosticsisbasedontheanalysisoffailuremodes,detectionofearlysignsof

wearandaging,andfaultconditions.Thedisciplinethatlinksstudiesoffailuremechanismsto

systemlifecyclemanagementisoftenreferredtoasprognosticsandhealthmanagement

(PHM).Technicalapproachestobuildingmodelsinprognosticscanbecategorizedintodata

drivenapproaches,modelbasedapproaches,andhybridapproaches.


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1.2.1 DataDrivenPrognosticsDatadriventechniquesutilizemonitoredoperationaldatarelatedtosystemhealth.

Datadriven

approaches

are

appropriate

when

the

understanding

of

first

principles

of

system

operationisnotcomprehensiveorwhenthesystemissufficientlycomplexthatdevelopingan

accuratemodelisprohibitivelyexpensive.Themethodmodelscumulativedamageandthen

extrapolatingouttoadamagethreshold.

However,aprincipalbottleneckisthedifficultyinobtainingruntofailuredata,in

particularfornewsystems,expensivesystemsandhumaninvolvedsystems,sincerunning

systemstofailurecanbealengthy,rathercostlyanddangerprocess.Thesedatasourcesmay

includetemperature,pressure,oildebris,currents,voltages,power,vibrationandacoustic

signal,spectrometricdataaswellascalibrationandcalorimetricdata.Featuresmustbe

extractedhighdimensionaldata.

1.2.2 ModelbasedprognosticsModelingphysicscanbeaccomplishedatmicroandmacrolevels.Atthemicrolevel,

physicalmodelsareembodiedbyseriesofdynamicequationsthatdefinerelationships,ata

giventimeorloadcycle,betweendamageofasystem/componentandenvironmentaland

operationalconditionsunderwhichthesystem/componentareoperated.Themicrolevel

modelsareoftenreferredasdamagepropagationmodel.Macrolevelmodelsarethe

mathematicalmodelatsystemlevel,whichdefinestherelationshipamongsysteminput

variables,systemstatevariables,andsystemmeasuresvariables/outputswherethemodelis

oftenasomewhatsimplifiedrepresentationofthesystem.Theresultingsimplificationsneedto

beaccountedforbytheuncertaintymanagement.

1.2.3 HybridapproachesHybridapproachesattempttoleveragethestrengthfrombothdatadrivenapproaches

aswellasmodelbasedapproaches.Agoodexamplefortheformerwouldbewheremodel

parametersaretunedusingfielddata.Abadexampleforthelatteriswhenthesetpoint,bias,

ornormalizationfactorforadatadrivenapproachisgivenbymodels.

1.3 ApplicationFieldsInthisresearch,weimplementedourprognosticalgorithmsonlithiumionbatteriesand

millingmachinecutters.TheLithiumionbatterywaschosenbecauseitissopopulararoundthe

world.Wheneveruselaptopcomputeronbattery,peopleunderstandthebatteryremaining

usefullifeterms.Alsothetestsetupisrelativelyeasyandaccurate.Runtofailuredatais


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inexpensive.Inordertodescribeourresearchapplications,itisnecessarytointroducethebasis

ofthesetwofields.

1.3.1 LithiumionBatteriesIn1912,G.N.Lewisbeganpioneeringworkonthelithiumbattery.Thenon

rechargeableversioniscommerciallyavailableattheearly1970s.Howeverthelithium

batterieshavefacedabigsetbackduetoissuesinvolvingsafety.Becauseoftheinstabilityof

lithiummetalduringcharging,researcheffortsshiftedtoanonmetalliclithiumbatteryusing

lithiumions.Attemptstodeveloprechargeablelithiumbatteriesfollowedinthe1980sbutthe

endeavorfailedbecauseofinstabilitiesinthemetalliclithiumusedasanodematerial.In1991,

theSonyCorporationcommercializedthefirstlithiumionbattery.

Theinstability

of

lithium

metal

shifted

research

to

anon

metallic

solution

using

lithium

ions.Althoughlowerinspecificenergythanlithiummetal,Lithiumionissafe.Batterypackers

usuallyputaprotectionchipinsidebatterycelltokeepvoltageandcurrentstosecurelevels.

AfterSonycommercializedthefirstLiionbattery,thischemistryhasbecomethemost

promisingandfastestgrowingonthemarket.

Figure1 BatteryEnergyDensityComparison

ThespecificenergyofLiionistwicethatofNiCdandthehighnominalcellvoltageof

3.6Vascomparedto1.2Vfornickelsystemscontributestothisgain.Improvementsinthe

activematerialsoftheelectrodehavethepotentialoffurtherincreasesinenergydensity.The

loadcharacteristicsaregood,andtheflatdischargecurveofferseffectiveutilizationofthe


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storedenergyinadesirablevoltagespectrumof3.70to2.80V/cell.Nickelbasedbatteriesalso

haveaflatdischargecurvethatrangesfrom1.25to1.0V/cell.

In1994,thecosttomanufactureLiioninthe18650cylindricalcellwithacapacityof

1100mAhwasmorethan$10.In2001,thepricedroppedto$2andthecapacityroseto1900

mAh.Today,highenergydense18650cellsdeliverover3000mAhandthecostshavedropped

further.Costreduction,increaseinspecificenergyandtheabsenceoftoxicmaterialpavedthe

roadtomakeLiiontheuniversallyacceptedbatteryforportableapplication,firstinthe

consumerindustryandnowincreasinglyalsoinheavyindustry,includinghybridelectric

vehicles.

In2009,roughly38percentofallbatteriesbyrevenuewereLiion.Liionisalow

maintenancebattery,

an

advantage

that

many

other

type

batteries

cannot

claim.

The

battery

hasnomemoryanddoesnotneedexercisingtokeepinshape.Selfdischargeislessthanhalf

thatofnickelbasedsystems.ThismakesLiionwellsuitedforfuelgaugeapplications.The

nominalcellvoltageof3.60Vcandirectlypowercellphonesanddigitalcameras,offering

simplificationsandcostreductionsovermulticelldesigns.Thedrawbacksaretheneedfor

protectioncircuitstopreventabuse,aswellashighprice.

AgingisaconcernformostLithiumIonbatteries.Somecapacitydeteriorationis

noticeableafteroneyear.Thebatterymayfrequentlyfailovertwoperhapsthreeyears.The

lossofchargeacceptanceoftheLithiumIonbatteriesisduetocelloxidation,whichis

permanent.Theestimationofhowmuchchargeacceptancecapacityandthepredictionofhow

longabatterycanbeusedareinterestingresearchtopicsandmanyresearchersareworkingon.

Thepredictionofthiscapacityiscrucialinsomemissioncriticalapplications,suchasouter

spacevehicles,unmannedaerialvehicles(UAV)andportablemilitarydevices

1.3.2 MillingMachineCuttersAreliableandintelligentmonitoringsystemisveryimportantforcuttingprocess.A

successfulmonitoringsystemcaneffectivelyestimatetoolwearprogress.Inalotof

applications,suchasmillingmachinesthatcutoneworkpiecewithoutchangingthecutterin

themiddleoftheprocess,themonitoringsystemmustpredictthecutterremainingusefullife.

1.4 PreviousResearches1.4.1 EarlyBatteryResearches

In1990,T.Matsushimaetal.introducedanddemonstratedrechargeableleadacid

batteriesremainingusefultimecalculationinamperehoursmethod[1].In1994animproved


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stateofchargeindicatorwasproposedbyT.B.Atwateranditwaspatentedin1995[12].K.

KutluaypublishedonlineestimationofLeadAcidbatteryremainingusefullife[3].In[4]and[7],

twonoval

battery

model

were

discussed

for

battery

prognostic.[6]

and

[11]

are

real

prognostic

applicationusageinHybirdElectricalVehicleandElectroMechanicalActuators.

1.4.2 NASA AmesResearchCenterSeveralpredictionmethodsareavailable,ofwhichthemostcommonarebasedon

batteryinternalresistance.However,theimpedancemeasurementaloneprovidesonlyarough

sketchofthebatterysperformance.Forexample,afullychargedbatterythathasjustbeen

removedfromthechargershowsahigherimpedancereadingthanonethathasrestedfora

fewhoursaftercharge.

1.4.3 ResearchesforMillingMachineCutterPredictionVariousmethodshavebeenstudiedintheareaoftoolwearestimation.XiangLietal.[2]

presentedthemethodofwaveletpackettransformforonlinewearingpredictionofahigh

speedmillingcutter.Highspeedcutterwearingmechanismsinphysicsmodelswereaddressed.

However,noneofabovepaperpredictedremainingusefullifewithrespecttotheasymmetric

penaltyfunctionasinthe2010PHMdatachallenge.Thispaperdescribesouralgorithminfull

andpaysspecialattentiontotheconnectionbetweenprognosticanddiagnosticproblems.

1.5 Prognosticsapplicationenvironmentandsetup1.5.1 NASAAmesResearchCenterBatteryTestbed

NASAPCoEpublishedadatasetof34batteriesasofthisthesisiswritten. Allbatteries

werechargedinaconstantcurrent(CC)modeuntilthebatteryvoltagereached4.2Vandthen

continuedinaconstantvoltage(CV)modeuntilthechargecurrentdroppedto20mA.

Impedancemeasurementwascarriedoutthroughanelectrochemicalimpedancespectroscopy

(EIS)frequencysweepfrom0.1Hzto5kHz.Thedatawascollectedduringtheperiodofspring

2009tospring2011.

Thesebatteries

were

run

through

3different

operational

profiles

(charge,

discharge

and

impedance)atdifferentambienttemperature(4,24and43degreeC).Dischargewascarried

outat1Amp,2Ampor4Ampuntilthebatteryvoltagefelltoacertainvaluerangedfrom2.0V

to2.7V.

TheindividualtestdiagramandbatterytestprofilearedescribedinappendixA1.


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1.5.2 2010PHMDataChallengeCompetitionThe2010PHMDataChallengeCompetitionprovidedmillingmachinedata.Thetestbedis

similarto

the

one

used

in

paper

[2].

Cutters

are

6mm

ball

nose

tungsten

carbide

cutter.

The

endofthesecuttersishemispherical,whichisidealformachineswith3dimensionalcontoured

shapes.Eachcutterwasusedrepeatedlytocutthesameworkpiecelittlebylittle.Thespindle

speedofthecutterwasabout10400rotatesperminute(RPM).Thefeedratewas1555mmper

minute.Ydepthofcutwas0.125mmandZdepthofcutwas0.2mm.Figure2showsthe

coordinatesofX,YandZ.Datawereacquiredat50kHzforallsevenchannels.

Figure2 CuttingToolCoordinates

Therearesixindividualcutterrecords,namedc1,c2...c6.Recordsc1,c4andc6are

trainingdata,andrecordsc2,c3,andc5aretestdata.Eachtrainingrecordcontainsone"wear"

file

that

lists

wear

after

each

cut

in

10

3

mm,

and

315

individual

data

acquisition

files.

In

total

1890cutfileswereprovidedtoallparticipants.

2 ProblemDefinition2.1 GeneralPrognosticProblemFormulation

Theprognosticproblemisanoptimizationproblem.Whenasystemisrunning,prognostic

algorithmshouldcollectdatainrealtimeandgivethebestpredictedremainingusefultime,

whichshouldminimizethepossiblecostofunderestimateandoverestimate.

Minimize U U O O f t P t C P t C (1)

where tispredictedremainingusefullife; f t isthetotalestimatedcost. UP t isthe

probabilityofunderestimateand OP t istheprobabilityofoverestimate.The UC and OC are

underestimatecostandoverestimatecostrespectively

Differentfromotherestimationproblems,thecostfunctionisusuallyunsymmetrical.

OC couldbelargerthan

OC inmanytimes.Takeanexampleforaircraftbatteryremaininguseful


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lifeestimation.Underestimatewillresultinjustexcessivewastedfuelbutoverestimatecould

resultincrashorpeopleindanger.

2.2 LithiumIonBatteryModelWefoundusuallybatterydischargeprofileisprettyconstant.Thereisusuallyafilter

circuitinbetweenbatteryandload.Itflatsthedischargecurrent.HencethebatteryDC

characteristicismoreofaconcern.InFigure3thesimplestmodelisproposed.

Figure3 SimplifiedBatteryModel

Abatteryhasanopencircuitvoltage.Itisusuallynotmeasurableexternallybutitcanbe

calculatedwheninternalimpedance ER isknown.

2.3 LithiumIonBatteryKeyParameters2.3.1 OpenCircuitVoltage(OCV)

OCVismeasuredwhennoelectricloadappliedtobattery.OCVindicatesthebattery

remainingusefullife.SometimetheOCVissuperficialandhardtoestimate.Weproposeda

waytodynamicallyonlineestimatetheinternalresistance.Duringdischargephase,we

introduceasmallamount(+/10%)loadripple.Thecorrespondingvoltageandcurrentchanges

canbeusedtocalculatebatteryinternalimpedance.

int

VR

I

(2)

Knowingthe

impedance,

OCV

can

be

calculated

as

intOCV TermU U I R (3)

2.3.2 ChargeandDischargeCapacityThebatteryrealtimeStateofCharge(SoC)isnotmeasurablebytoolsdirectly.However

SoCdifferencebetweentwotimepointsiscalculablewithcurrentandtimeinformation.


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9

1

N

n n

n

C I t

(4)

2.4 MillingMachineCutterKeyParametersForthecutterwearing,weneedtoestimatethemaximumnumberofcutsonecould

"safely"makeforagivenwearlimit.By"safely",itmeansthatthemaximumwearofanyflute

doesnotexceedthewearlimit.Thepenaltyforoverpredictionissmallerthanthatofunder

prediction.

TheestimationerrorisdefinedasEq.(6)

Estimated Actuald n RUL n RUL n (5)where n isawearlimit. d n istheestimationerroratwearlimit n .Whenoverestimated,

i.e.theestimatedRULismorethantheactualRUL, d n ispositive.Whenunderestimated,

d n isnegative.ThepenaltyfunctionsaregivenasymmetricallyasEq.(2).

/10

/4.5

, 0

, 0

d n

d n

e d ns n

e d n

(6)

Thetotal

score

is

calculated

by

summing

up

all

penalties

and

least

score

wins.

165

66n

S s n

(7)

Thereisnopresetscorebenchmarkforthiscompetition.Bytestingthecuttingcycle

predictionscoresofcutter2,cutter3andcutter5,thebestalgorithmsareselectedin

professionalteamandstudentteam.

3 ApplyLinearRegressiontoPrognostic3.1 UseEstimatedRemainingCapacityastheRegressor

Linearregressionallowsustoestimate,andmakeinferencesaboutpopulationslope

coefficients. OuraimistoestimatethecausaleffectonYofaunitchangeinX.Westartfrom

fittingastraightlinetodataontwovariables,YandX.

HerewechoosetimeasX,estimatedcapacityastheregressorY.


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Figure4 BatteryDischargePlot

Figure4showsatypicalbatterydischargecurve.Theblueline(withstarmarkers)

indicatesthevoltagegraduallygoesdown.Theredline(withxmarkers)showsthisisaconstant

currentdischargeat2Amprate.Thegreenline(withcirclemarkers)isthecumulativeused

capacity.

3.2 RegressiontoEstimateEndofChargeTimeForeverynewdischargecycle,wesetthesampletimeasX.Bycomparingthecurrent

voltagewithlastcyclevoltage,weestimatedcurrentdischargecapacityasregressorY.Thenwe

uselinearregressiontopredictatwhattimetheestimatedcapacityreachesthelimit,whichis

availablefromlastdischargecycle.


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11

Figure5 PredictiononSyntheticData

Figure5showsthelinearregressionprogress.Itshowsattimet=100,astraightlineisfit

toexisteddata.Byextrapolationitispredictedattimet=150,thekeyparameterreachesa

presetlevel.Forbatteryprediction,wepredictatwhattimethecapacitywillreachlastcycle

value.

3.3 RegressionResultonNASAbatterydataEachsetofNASAbatterydatahas200+dischargecycles.Figure6showsthebattery

B0005predictionresults.Thexaxisisactualremainingusefultimeinseconds.Theyaxisisthe

predictedremainingusefultime.

Figure6 BatteryB0005PredictionResult

WeusehistogramtodemonstratethepredicationresultasinFigure7,Figure8and

Figure9.

The

Xaxis

is

the

prediction

error.

Less

than

zero

is

underestimating

and

above

zero

is

overestimating.


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Figure7 RULerrorat2000sectoend

Figure8 RULerrorat500sectoend


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Figure9 RULerrorat100secondstoend

4 UNOBatteryTestPlatform4.1 Motivationofbuildingthetestplatform

Ibelievethefirsthanddataiscriticaltodatadrivenprognosticresearch.Thereareafew

batterytestbedsweresetinotherresearchfacilities.Butalocalbatterytestbedwouldenable

ourresearcherstoimmediatelyverifythealgorithmsandtocultivatemorethoughts.Buildinga

batterytestplatformcostnotmuchbutitwillbethefirstplatformforprognosticpurposein

UniversityofNewOrleans.

Astheprognosticandhealthmanagementsciencebranchbecomestrongerandstronger,

Ibelievemoreresearcherswilljointhisfield.Andmorestudentsmaychooseitastheirresearch

interest.UniversityofNewOrleanshassetuptheResearchExperienceofUndergraduates

problemtorecruitagroupofdiverse,talentedundergraduatestudentsfromaroundthenation,

andtoactivelyengagetherecruitedstudentsintocuttingedgeIntegratedSensingand

AutomatedSceneUnderstanding(ISASU)research.Thisbatterytestplatformwillbeavery

goodtoolforsomeofthemtopracticeprognosticresearches.

4.2 TestcapabilitiesToconductbatterytestandcollectdataforprognosticpurpose,followingobjectiveswereset.

Keepuptofourbatteriesinsideacontrolledtemperatureenvironment Temperaturecanbesetfrom2degreeCto60degreeC.Hysteresisis1degreeC. Chargervoltagefrom1.2Vto22.2V,currentlessthan5A,totalpowerlessthan50W.


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Dischargerworksfrom1.2Vto22.2V,dischargecurrentlessthan10A.Totalpowerlessthan100W

16bitDACtokeepvoltageandcurrentsensingresolution0.025%,accuracy1%.Temperatureaccuracy1degreeC.

MATLABandLabViewInterfaceforcontrolandmonitoring.4.3 TestSetup

Thetestsetupisasfollowingfigure.AKWC2512Vfridgeisusedasconstanttemperature

chamber.AComputerpowersupplyprovidesallpowertorelaysandchargers.NIUSB6009

DataAcquisitiondevicecollectsvoltage,currentandtemperaturereadingsandcontrolsrelays.

Figure10 UNOBatteryTestPlatform

4.3.1 VoltageControlCurrentServoTocontrolthebatterychargeanddischargecurrent,avoltagecontrolledcurrentsource

isdesignedasfollowingschematic.ThecontrolvoltageisfromUSB6009analogoutputport.

ThecircuitwillkeepthevoltageonR3equivalenttotheinputvoltage,sothatacontrollable

currentisgoingthroughthetargetdevice.


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Figure11 VoltageControlCurrentServo

4.3.2 ConstantCurrentandConstantVoltageChargerCircuit

Figure12 ConstantCurrentandConstantVoltageChargerCircuit

TheCCCVcircuitisdesignedforlithiumionbatterycharging.Whenbatteryvoltageisless

than4.2V,IC2willnotfunctionsothatLM317blockisrunninginconstantcurrentmode.It

keepsvoltage

across

R1+R2

into

1.25V,

which

makes

1.25A

charging

current.

When

battery

reaches4.2VtheIC2willmakeLM317intoconstantvoltagemode.ThevoltageacrossR5and

R4willnotexceed4.2V.

4.3.3 ImpedanceMeasurementDuringDischargeCycleEvery5minutesduringdischargephase,a+10%and 10%rippleisappliedtodischarge

currentcontroller.Thecorrespondingvoltagechangesareusedtocalculatebatteryinternal

impedance.


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4.4 TestProceduresAMATLABcodeiswrittentocontroltheUSB6009DAQactionsandtocollectallanalog

inputvalues.

Detailed

algorithm

is

omitted

in

this

thesis.

In

general

the

procedure

is

Initializeallrelaysandselftest Enablechargercircuitandcollectvoltage,currentandtemperature Afterbatteryisfullycharged,restforatleast30minutesandwaittemperaturebackto

constant

Dischargethebattery.Addimpedancetestwaveformsevery5minutes.Collectalldataandprotectbatteryfromoverdischarging.

Rest30minutesandaftertemperaturebacktoconstant,startanothertestcycle5 ConclusionsandFutureWork5.1 Conclusions

Byapplyinglinearregressiontobatteryandmillingmachineprognostics,wefoundthat

whenlastcycledischargecapacityisknown,thepredictionaccuracycanbeveryaccurate.At

1000secondsbeforeendofcharge,theerrorisnomorethan50seconds.Thelinearregression

performanceis

satisfactory.

AbatterytestplatformwasdevelopedatISLforprognosticdatacollectionandalgorithm

development.WenoticedthattoestimatebatterySOCisakeytoEndofChargeprediction.

TheexistingbatteryprognosticdatasetsfromNASAdidnotprovidereliablebatteryimpedance

data,anditishardtoidentifyoutliers.Usingourowntestplatform,wecangenerateaccurate

batteryusagedatawithrealtimemeasurementsoftemperatureandinternalimpedance.

Duringtheperiodofprognosticresearch,Ijoinedthe2010PHMDataChallenge

CompetitionandIwonfirstprizeinthestudentcategory.Isubmittedapaperdisclosingthe

winningstrategy

to

Int.

Journal

of

Prognostics

and

Health

Management

(ijPHM).

Iapplied

for

ThesisImprovementGrantfromgraduateschoolofUniversityofNewOrleansandtheproposal

wasawarded.

Thisthesisdescribedthelinearregressionapplytolithiumionbatteryandmilling

machinecutterwearingapplications.Followthesamemethodmanyotherprognosticproblems

canbesolvedinsimilarmanner.IhopethisthesiscanbeabridgeforreaderssolvemorePHM

challenges.


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5.2 FutureWorkIexpecttoexpandthethesisworkinthefollowingavenuesiftimeallows.

a) Applyweightedlinearregressiontoimprovethepredictionaccuracy.Currentlythealgorithmsetsallpointsequalweights.Earlytimedatakeepimpactthewholestraight

linefitting.

b) Analyzetherelationshipbetweenbatterytemperatureandinternalimpedancesoastoestimatebatteryopencircuitvoltagemoreaccurately.Itwillreducethestateof

chargeestimationerrorsothatremainingusefullifecanbemoreaccurate.

c) Forthebatterytestplatform,thelongtimerunningreliabilityneedtobeimproved.The

USB

bus

is

still

not

reliable

enough

to

sustain

long

time

testing.

A

dedicated

systembus,orembeddedmicrocontrollerwillenablelongtermreliablecycletest.


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Bibliography[1]

Toshio

Matsushima,

"PREDICTION

OF

REMAINING

BATTERY

DISCHARGE

TIME

IN

TELECOMMUNICATIONSSYSTEMS",1990.

[2]XiangLietal.,"FuzzyNeuralNetworkModellingforToolWearEstimation,"inAnnual

ConferenceofthePrognosticsandHealthManagementSociety,2009.

[3]KorayKutluay,YigitCadirci,YakupS.Ozkazanc,andIsikCadirci,"ANewOnlineStateof

ChargeEstimationandMonitoringSystemforSealedLeadAcidBatteriesin

TelecommunicationPowerSupplies",vol.52,No.5,2005.

[4]Fotis

Kerasiotis,

Aggeliki

Prayati,

Christos

Antonopoulos,

Christos

Koulamas,

and

George

Papadopoulos,"BatteryLifetimePredictionModelforaWSNPlatform",2010.

[5]NormanR.DraperandHarrySmith,AppliedRegressionAnalysis,3rded.WileySeriesIn

ProbabilityAndStatistics,1998.

[6]HaifengDai,XuezheWei,andSunzeChang,"ANewSOHPredictionConceptforthePower

LithiumionBatteryUsedonHEVs",2009.

[7]MinChenandGabrielA.RinconMora,"AccurateElectricalBatteryModelCapableof

PredictingRuntime

and

IV

Performance"

in

IEEE

TRANSACTIONS

ON

ENERGY

CONVERSION,2006,vol.21,No.2.

[8]IsidorBuchmann,BatteriesinaPortableWorld:AHandbookonRechargeableBatteriesfor

NonEngineers,2nded.CadexElectronicsInc,2009.

[9]PatrickJ.VanBree,AndreVeltman,WillH.A.Hendrix,andPaulP.J.VanDenBosch,

"PredictionofBatteryBehaviorSubjecttoHighRatePartialStateofCharge",vol.58,No.

2,2009.

[10]PieroP.BonissoneandKaiF.Goebel,"Whenwillitbreak?AHybridSoftComputingModel

toPredictTimetobreakMarginsinPaperMachines",2002.

[11]EdwardBalabanetal.,"ADiagnosticApproachforElectroMechanicalActuatorsin

AerospaceSystems"2009.

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[13]BayramAKDEMIR,SalihGUNES,andIsmailComlekcilerTaha,"MicrocontrollerCompatible

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Mining,

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AppendicesA1. NASATestBedCircuit

InNASAPrognosticCenterofExcellencewebsite,thereisnotexplicitschematicofthe

testbed.Inordertobetterunderstandtheironlinedata,thefollowingschematicsare

drewafteronsitevisitanddiscussiontoNASAresearchers.

Figure13 NASAAmesPCoEBatteryTestbed

A2. TestBatterySpecificationWeused3.7V2200mAhTenergyLiion18650Cylindricalbatterywithtabsinourtestruns.This

appendixgivesthebatteryspecificationforreference.

Illustration1 Tenergy3.7V2200mAHLiion18650Battery


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Features LiIon18650cylindricalrechargeablebatterywithtabs; 3.7V2200mAhhighcapacity Highenergydensityandlowerweightthanotherrechargeablebatteries ManufacturedunderISO90012000toassurequality UL#MH48285 BatterytestedbasedonInternationalElectrotechnicalCommission(IEC)standardto

ensurecapacity,quality,andlifetime

Applications BuildingLaptopBattery Buildingportablepowerdeviceneedinghighenergydensityandlowweight

ProductSpecifications Capacity*Nominal 2200mAh,Minimum2150mAh Dimensions: Diameter18+/ 0.2mm Height65+/ 0.2mm Weight(Typical)Approx.46gyes NominalVoltage:Average3.7V CutoffVoltage:3.0V InternalImpedance:lessorequalto180 milliohm(withPTC) CyclePerformance:90%ofinitialcapacityat400cycles Cyclelife:>500cycles Charge:Current=0.5CmAVoltage=4.2VEndCurrent=0.01mA Discharge:Current=0.5CmAEndVoltage=3.0V Max.Chargingcurrent:1.5Cma Max.Dischargingcurrent1.5Cma(forcontinuousdischarge)


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VITATheauthorwasborninNingxiang,Hunan,China.HeobtainedhisCollegediplomain

computersciencefromHunanUniversityin1995andMastersdegreeincomputerscience

fromDonghuaUniversityin2004,respectively.HejoinedUniversityofNewOrleanselectrical

engineeringprogramtopursueaMastersDegree,andbecomeamemberofAssociate

ProfessorHuiminChensresearchgroupin2009.BeforethathewaswithGeneralElectricalasa

teamleaderandprogrammanager.

Documents

A Study on Remaining Useful Life Prediction for Prognostic Applic