RISK-BASED FA LT DI

RISK-BASED FALTDIG,'0ISA,'DSAFETY

~IAj'AG E ~IEjTFOR PROCESS SYSTE;\IS

by

Athcsis submittedtolhcSchool of Gradual c Studi csi np art ialfulfilhncnt o f

the requirement sforthedegreeof

j' l l.1 stt' rofEII~i nt'eri ng

Facultyof Enginee ringand AppliedScience

MemorialUniversityof'cw found land

Today,plantsinchemica land processindustry are becominglargerandmore complex Corollaryofthistrendimplies thateach hour of downtime ismore expensive.As industrialsystemsenlarge,the totalamountof energyand materialbeinghandled increases,makingfaultdiagnosis andsafety management conside rably importantboth fromthe viewpointof process safety as wellas economic loss.Therefore,seekingan effectiveapproachtoperformfault diagnos isandimplementsafetymanagementis importan t andimperat ive. Aninnovati ve methodol ogy of risk-based SPC faultdiagnosis anditsintegrationwith SafetyInstrumented System (SIS) is proposedinthisthesisto

Unlikeanyexistingfault diagnosis and safetymanagemen t approaches.theproposed methodologyp ioneers abrandnewpathway for lhe fauhdi agnosis and safety managementin process industry. Thisproposedmethodologyneitherdepends onany processmodel asmodel-basedmethods, nor depends on large amountof historical processdata as conventiona l processhistorybasedmethod. Controlchart techniqueis usedtodistinguishabnormal situationfrom normal operationbascdonthree-sig ma rule andlinear trend forecast. Time seriesandmoving average teehniques areused toperform real time monitoringandnoisefilteringinfault diagnosis process.Furthermore, risk indicators areu sedt oid entify andd etemline potential fault(s)t om inimize thc number of

Theproposedmethodology o f risk-based SPC fault diagnosis and itsintegrationwith safety instrum ented systemsis implemented using 02development cnvironmcmTotcst andverifythis methodology, a tank filling systemanda steampowerplant systemwith SISl sandSlS2sare developed in02 environment.A techniq uebreakthrough,from univariatemonitoringtomultivariatemonitoringfor SPC fauitdi agnosish asb ccnm adc in the verification inthesteampower plant system

Ackno wledg em ent s

Asapcrsonwhodesirestodedicatehcrsclftoarescarchwhich is hcr dream In her Iifc, thebiggest wish forher is to have anopportunitytopursue this dream and make it true.

The School of Graduate Studies ofMemorial University of Newfoundlandprovidesthis opport unity tothe author.so please allow the authortoexpress hersincere appreciationto himfirst.

Dr.Faisal Khan andDr.Tariq Iqbal. twoofthe most outstandingprofessors in the Faculty of Engineering andApp lied Science atMemorial University of Newfoundla nd.providcd thcauthorwith dctailed guidance and also financialsupport for her research.Herein, please accept the thankfulness from theauthor's heart to them.

Appreciations are also shown toDr.YanjunChang. Mr.CenIan and allthcIricnds who have everhelped the autho rinherresearch andher life.

Thankstheauthor'sfamil y.withouttheunderstand ing andsuppon fromthem,theauthor wouldnothave thestudy opportunityillMemoria lUniversity of New foundland.

J.ISafctylnstrumentcdS ystcm 2 1.1.1ProccssControi Systcm nnd SIS... .. . .3 1.1.2Risk and RiskReductionMethods....

1.1.3SafclyFun clion (SF)....

1.2.2RiskRcductionTcnn s and Equations 9

1.2.3SafcryIntcgrityLevelI'Sfl.) 10

1.2.4EventTree Analysis (ETA).... . 13

IJSt3tisticaI ProcessConuol 15

.•••..••..•.. •... .•... .•.... .. ...•... ...•... ... 15

13.2ControIChan... . . . 16

...•.•... ... ... ... ... . ...17

1.3AMovingAvera ge Techniques 18

l.aObjecuvesof'Ibis Rescarch 25

1.5Organization ofThis Thesis 26

Ch li pter 2 i\1t'1 hodolog)·or Risk.basedS PCrauIl Oia~nosisa ndSaret,";\ lan aJ:emen t

2.JReiewofExistingFauhDiagnosisMcthods 17

2.2ProposedMethodology.. . .. 2.3Verificationof Proposcd FaultDiagnosisMethodology... .

2.3.1Fault Diagnos isPrinciple...

2.3.2 SPC Fault Diagnosis.. .. .. 38

2.3.3Ri sk-bascdSP CF auItDi agnosis.... .. . .. .41

2AG2 Development Environmcnt... . . .A8

Cha pter3Implem entation and Verificationor uieProp osed;\le1hod oJ0t::,-inG2 OCHlopm e nt En,"iron mc nt-Tank Fillin t::S,-S1tm

3.1Rcquirements to the Tank FiJlingSy stcm....

3.2Dcterminislic Dc"cJopmcnIStagc.... .

3.3SPC DcvclopmcnISlagc.... ... .. 57

3.4Risk-basedSPCDevelopm ent Stage 59

3.4.2 ThcDcvclopmcnlof SIS I. 64

3.4.3Th cDcvclopmcnlofSIS2 67

...69

3A.5Comparisonwilh CcnNan'sW ork 70

Cha ple r " Implem ent ation and\'enOcationeftheProposed;\Icth od olog)'inG2 Dev el opmentEn viro n me n t- SteamPowerPlantSyste m

·URt.-quircmcntstolhcSt camPowcrPlantSystcm 7-l

-l.2Console Constructi onin G2 Environment. 77

--- ---- - -~

4AThclmplcmcntalion of thcProposcd~lcthodology 81

4.4.ISPCDcv clopmcnIS tagc 81

4.4.2Risk-basedSPC Developme ntSragc .,. . . 87 4.4.2.ICharactc risticF unct ion sandFauItDc finilion 87 4A.2.2ThcDcvclopmcnl ofSISI&SIS2.. .. .. 90 4.4.2.3Comparisonwith TraditionaIApproach 95

... ... ... .. .. .. ...103 ... . . . ... ... ... . ....106

List

or

Figu res

Fig.IMainPans ofaSafctyInslcumcnlcd Syslcm. Fig.2S cparat ion o fBP CS andP rotcct ion Systcm. Fig.3Basic51S Layout. . . Fig.4 5afcty Protcc tivc Laycrs. . . . . Fig.5RiskRed ucti on.... Fig.6Determin ation ofSIL...

Fig.7An Example ofEvent TreeAnalysis . Fig.8AnExampl c o fControI Chan .

...3 . ...3

•••.••••• ..••••• ..••...•..•••11 ...12

. 14

• •...16 Fig.9 Time Seri es:randomdataplus trcnd,with best-fitline and difTcrcnt smooth ings

Fig.lOAn exampleofMoving Average ofStockPrice Chart. . .. . 19

Fig.I IW MA Wcights... . . 22

Fig.12 EMAW cights 23

Fig .13 Classificatio nofDiagn ostic Algorithms(Vcnkatasub ramanian ctal.,2003)

Fig.15Methodologyof theRisk-basedFaullDiagnosisand SafetyManagementfor

Fig.16 Standard DeviationDiagram.... ..37

Fig.17 Nonn a lity Test for SteamPressur eData inNormal Situation .38 Fig.18Normalit yTestforSteamPressureDatain Abnorm alSituation 39 Fig.19LineChartforMovingAve rage Steam PressureDa ta inNorma l Situarion..AO Fig.20 Line Chart forMoving Average SteamPressureData inAbnonna I Situation

Fig.2 I Erro r Function 43

Fig.22In'cgrand j=cxp (-z') 44

Fig.204G2 Platfonnfrom GensysmCorporation .048 Fig. 25 Console of the Tank LevelMonitor withBPCS . Fig.26 Console ofthcTankLevelMonitor with BPCS&SISI.. . . Fig.27ConsoleofthcTank Lc"c I Monilo rwithBPCS&S ISI&S IS2 .55 Fig.28 ControlChanfor the Tank FillingSyslcm.. . . .. . . 58 Fig.29 Risk-based SPC FaultDiagnosis and SISsfor Tank FillingSystem..

Fig.30AnExamplcofRulcDclinitionin Tank FillingSystcm....•..

Fig.31 Data Points in TimcOrdcr. . . . .. 63

Fig.32 Risk-basedTank Levcl Comrol Cban....

Fig.33 Risk-basedTank Level Trcnd Chan-SISI. .

Fig.34PrcdiclcdRiskChan-SISI 66

Fig.35 Risk-basedTank LcvcITrcndChan-SIS2. . . 68

Fig.36Prcdiclcd RiskChan-SIS2 69

Fig.37Risk-bascd Tank Lc"c1TrcndChan-SIS2 70

Fig.38 Consolcof thcTan kFillingSystc minCcn Nan'sWork 71 Fig.39St camP owcrPl anlinll1cnn odynamics 3nd Fluidslab 74

Fig.40SchematicDiagramof theSteamPowerPlant 75

Fig.41Consolcofthc 5tcamPowerPl ant 5 ystcrn.. .. . 78 Fig. 42lIistoricalData ChanfortheBoiler StcamPressure 79 Fig.43 Characteristicsof an Under-damped Response 80 Fig..t4Control Chan of the Steam Pressure in Steam Power Plant Syste m 82 Fig.o45ControlChan orthe Steam Pressurein Steam Power Plant System (Nonnal

Fig.o47CenNan·sSteamPowerPlantSystcm +lluizhiBao·sD iagnosisModulc 84 Fig.o48AssumptionsinCcn Nan's SteamPowerPlant System 85 Fig.o49FalscAlanninCen Nan's Steam Power Plant System 86 Fig.50AnExampleofRule DcfinitioninSteam PowerPlantSYslem.... . 89

Fig.5I Trend Charts for StearnPressure.SteamFlowRate and Steam Temperature&

RiskCarts forStea m Pressure.Steam FlowRate and Steam Temperature 91 Fig.52 StearnFJowRateTrend Chan and RiskChan.. .. . 92 Fig.53SIcam PressureTrend Chart and Risk Chart.... .. . 93

Fig.54Stcam TcmpcraturcTrcndChanand RiskChart 94

Fig.55 Unsaf e BoilerPressu reButton inCcnNan's KBRT systcm. Fig.56RiskValue vsSam pleNum ber Graphwith Base 100 Fig.57RiskValuevsSampleNum ber Graphwith Base e 114 Fig.S8Risk Value\'5Samp le Number Graphwith Base 100 116 Fig.59RiskValue vsSample Number Graph withBasec... .. . 116

- I

ALARPAsLow AsReasonablyPracticable API ApplicationProgrammer'sInterface BP Back-Propagation

BasicProcess ControlSystem CumulativeAverage CumulativeDistribution Function CumulativeMovi ngAverage ExponentialMovingAverage ESD

ETA EUe FDD

FDI Faull Oiagnosis andIdcnt ification GOA GzDiagnosticAssistant

Gateway StandardInterface Graph icalUscrln tcrfaccs GUIDEGraphical UscrInterfaceDevelopm entEnvironment leA IndependentComponent Analysis lEe IntcmationalElcctro-tcchnical Commission IFO InfonnationFlowDiagram ISC Intclligcnt Supcrvisory Coordinator KB RT Knowledge-BasedReal Time

l\1uhivariatcS tatisticalPr occss Contro l Occup ational Safety and IlealthA dministrat ion PrincipalC om poncnt Analysis

Safety Function Safety Integrity Level Safcrylnstrumcnted Systern Prebabilnyof Failures on Demand ProgrammableLogic Controllers PLS

PSM PrecessSafetyManagement RI

RRf SOD SF su,

impleMoving Avcrage SPC Statistical Process Control SSD Safety Shutdown UCl UppcrControllimit Ull

List of Symbols

C erf(x)

Unprotcctcd Ri k Frequency ProtectedRisk Frequency Tolerable RiskFrequency Probabiliryof Fauh PFDaoog. Probabilirycf Failure onDernand

Severityof Fault Mean

DampedNatural Frequency DarnpingCocfficicnt

List of Appendic es

Cha ptcr l l nt rod uction

In1987.RobcrtM .Solo\\'.3n cconomjstatthc Massachu~us l nstitutcofTechnology.

receivedtheNobelPrizeineconom ics for hiswork indeterminingthe sources of economicgrowth.ProfessorSolow concludedthatthebulkof aneconom y's growthis the rcsult o flcchnologicaladvanccs (Crowl andLouvar. 2002).ltisrcasonablc10conclude that thegrowthofan industryis also dependent on tcchnologicaJ advan ces.Thisis especially true in the chemicalindustry,which is entering an era of more complex proccsscs:highcrpressurc,morcrcaclivcchcmicals,andclI:oticchcmisuy .Mon:comp lcx proccsscs rcqu ircmorc complcx sa fcty ll~hnology.Many industriali stsevcn believc thal thedevelopmentand app licationof safety technology is actually aconstraint on the growthofthe chcrnicalindustry,

As chemica lprocesstech nologybecomesmore complex.chemical engineers willneeda moredetailedandfundamental understanding ofsafety.llowardII.Fawcett said."To know istusurviveand toignorefundamentalsis10court disaster."(Fawcettand Wood.

1982).Flixboroughdisastcr, wh ichhappenedin Englandin 1974,wasthewake-upcall for theUK.The incidentresultedin28deaths,over100 injuries andthecomplete destruct ionoftheplant.Thedeathtollfromthe Bhopal accident,which happcncdinIndia in 1984,was over 2,000at the time ofthe accident. Some recentreportsplace the estimatesas high as10.000 withover200,000inj uries.Chemobylaccident.which happcncdinSovi ctU nioni n 1986. is cstimably one a ft he worst industrial aceidcnts a f all timc.Pasadenaex plosian. whiehhappcnedinTeusinI989.waslhcwakc-upeallforlhe US ....ilh23 fatalitiesand130injurics.Anotheraccident in nearby Channelviewkilled 17 and injured over 100 less than one year later;These twa acciden tsresulted in the OccupationalSafetyandlIea lthAdministration(OSHA)PSM(ProcessSafety Managemcnt) JcgisJation(Gruhn.P,J999 ).

Asprocess safelyincidents arcstill happening today and as suchincide ntssometimes

leadtoseriousconsequcnccsforpcoplc,thccnvironmcntand propcrty ,itisconcludcd that the process industryhas a rcsponsibiluy to further reduce occurrcnccofthcse incidcnts.Duclothco bscrvcdchangingsitu3tioninthcproccssindustry,charactcrilcdby achanging kindofincidcn lsccnariosandc3u5Cs.anccdexislsforachanging kind of controlever process safcty(KncgtcringandPasman, 2009).

Inanincreasingly multidisciplinaryengineering environment.andin the face of ever incrcasingsystcmcomplc xity,thcrcisagrowingdcmandforcnginccrsandlcchnicians involvedinprocessengineeringtobeaware: of the implications of designing and operating safety-relatedsystems.SafetyInstrumen ted Systems play3vitalrole in providing theprotective layer functionality in many industrial process and au tomation

1.1Sa fetyI nst r ume nte dSyste m

TheInternational Electro-technical Commission(lEe)61508(2000 )standarddefin es Sa fety InstrumentedSystem(SIS)as"asystemcompo sedofsensors, logic solversand final-col1trolciclllcnts for thcpurposc or tukingtheprocess to a safestate,when predeterminl:o condi tionsare violatco".SISs arcalsocalledcmcrgcncy shuldown(ESD) systc rns,sa fetys hutdown(S SD}sys tcms,a ndsa fcty interlot'ksys terns

Safety instrumentedsystemslSIS)arcused inthe oil and gas indust ry to detectthe onset of hazardouseventsandlorto mitigatetheir consequences to humans , material assets,and thc cnvironmcnt (LundteigcnandRausand, 2007).A SISgenerally consist sofoncor more inputclements(e.g.,sensors,transmitters),one or more logic solvers {e.g., programmable logic controllers(PLe],relay logic systems),and one or more final elemcnts(e.g.,safetyvalves,circuitbreakers},asshowninFig.J .

A,,__

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SIS,look "..,..".Iy _ _ ... _ C_Sr-<IIl'('SI.

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... ---_.,..."' ^{.... .,... ..,.-}

_ _ _ . . . _.SlS ...

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f'I')""""··Ibo""'",lay""tof.,»"co1S1SI'~"'''<o><oootrollL~B·shu>Joo,o...,·¢. . ,h<f,nal ,,,,,"tll<I<n><m)

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Thc:orop<.r.SIS""""""'...Un.. """"""''''lI>ol_tro..., "'P"",h1'fOf bnnBLnB·proc<»"' ·.. r<>UI<;,,\h<"'·<II'or... """"cq>Lol!k""',.. "'f..l...

Safetycan be defined as "freedom from unacceptablerisk". This definiticn is Imponam, because it highlights thefact that allindustrial processes involve risk.Absol ute safety, where riskis complete ly eliminated,can neverbeachieved: risk canon Iybcreduced to an acceptable level.Therefore all risksshould be dealt with on the ALARPbasis,i.c.thc target isto ensurethat risk is reduced to Aslow As Rcasonably Practicablc.The ALARP principle provides a general objccrivc of SjS,which is to reducethc frcquencyat which a hazard may occur to an acceptable or at least a tolerablelevel.

Process risk is definedby the frcquency of the occurrence and the potentialcon sequence severity of the process hazard (Summers,2007).The formula for risk is:

Todefine thefrequency.the initiatingeventsarcidentifiedfor each processhazard . and their frequencyof occurrenceis estimated. The consequenceseverityisthe logical conclusionlothepropagalionoflheproccssha7.urdif noprotect ionlayers 'lre implementedasbarrierstotheevent.

SafetyMcthods emp loycd10protectagainst ormitigate harm/damage topcrsonnclcplant andthccnvironmcn t.an drcduccriskincludc·

•Changingthcproccssorenginccringdcsign

•Incrcasing mcchanical intcgrity o f thc systcm

•Dc\'cloping detailcd training and opcrationalp roccdurcs

•Incrcasingt hc frcquency o f tcsting o f critica l system componcnts

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... -.1100_01 _ _

50<_..,...•.,-•

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unsafcslalcloasafestatc( Marszalc lc.,2003).AsafetyfunClionworksasaprotC'Ction againslaspccificandidcotificdhazardousc\'cnt.hisamcthodlo dcfinethcfunctional relationship between inputsand outputsin SIS.Inputs canberegardedas scnsors.ourputs canberegardedas finalcontrolclementsand safety functioncanbcregardcd as a logic

SFis able to assist SIS10reducethe risks.The amountof risk reductioncanbe measured based on thecalculatcd Probability of Failures on Demand (PFD),which isthc probability thatSF fails tomaintain safc state when predetermined saferyconditions arc violalcd.Safctyfuncliononlyrcduccsriskandwillnc\'crcomplclelyeliminatcthe risk.

However,it wouldbesufficientto reduce thc risk to an acceptable level.

1.2Safety Ana lysis

Unliketheconvenientunitslike voltor kilogram,thereisno universal unitforrisk Scalesfor oneindustrymaynot suitthosein anoth er.Fortunat ely.themethod of ca1culation isgcncrally con sislcnt andit ispossiblctoarrivcatareasonable scaleof values fora givenindustry.Asa result,lEehave sugges ted usingasystemof risk classificationthatisadaptableformost safetysituations.Referring 10 Annex BofIEC 61508part 5,theriskclassificationtable isprovided as shownin Tablel.

Frequent)' Conseq uences

Catastrophic Critical Marginal Negligible

Frequent I I I II

Probable I I II III

Occasional I II III III

Remote II III III IV

Improbable III III IV IV

Incredible IV IV IV IV

Theriskclassificationmentioned in Table1is a generalizedversion that workslike follow ing:

• Determine(hefrequency clement oftheEUeriskwithouttheadditionof any protecti vefeatures(Fnp);

If. through using Table Lthis leads ro risk Class Lthen funherriskreduction Is requtred.

Riskclass IVor llJwouldbc jolcrablc risks.Risk classII wouldrcquirefunher

In practice, this Table I is a generic table for adaptationbydifferemindustry scctors.Ttis intended that any given industry sector should insert appropriate numbers intothe lidds ofthclablcandhenceeslablishacccptablcnorms.Fornamptc,inTablc2.somclrial

Frequency Catastrophic Critical .'lal"J:inal ~egliJ:iblr

>I dcath Idcathor Mine r injury Prodloss mj un cs

lpcrycar I I I II

1per5years I I II III

1per50years I II III III

Iper500years II III III IV

I per 5000years III III IV IV

Iper50000years IV IV IV IV

1.2.2Ri'kRedu ction Term ' andEq ua tion,

Thctennsandequationsthalcanbcuscdtodelincthcriskrcduetionareasfollo\lo

FtvTolcrableRisk Frequency

Fnp=UnprotectedRisk Frequency Fp=Protected Risk Frequency RRF:o::RiskRcductionFactor PFDavg.=Probabilityof Failureon Demand

RRF~Fnp/Ft (I-I) rFD3vg.-IIRRF ~Ft/Fnp (1-2)

1.2.3 Safelylntegr ity Level(SIL)

SILrcprcscntsthe amount of risk reduction that is required from a safety function.IEe 61508 defines SILas"adiscrttclc"cl(oncoffour)for spccifyiog thc safcry integrity rcqcircrncms ofsafcry function:'(2000).Safetyintegrity level 4 (Sll4) is the highest level and safety integritylevel I (Stl.Ijis ihe Iowcstonc.

SILhasbecomeincreasinglypan of'thedesign andoperationof safetyinstrumented system(Kirkwoodand Tibbs.2005),Companiesarcnow specifying SILhascd onInc amount ofrisk reductionthat isrequired to achievearolcrnblc risk level.TheSIS is designedtomeetorexceedthislevel ofperfon nancc.

Howdowedecide when 10 usc a safety instru mentedsystemand how goodmust itbe?

The answer is:itdepends on the amount of risk reduction required afterthe0thcr dcviccs havctx-cntakcnintoaccount.ThcmcasufCofthcamoumofriskrcductionprovidcdbya safctysyslcmiscallcdthcSafctylnlcgrity.andilisiJIuslralcdbyFig.5fromlEC.

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TIl<I~:e.tond<rdpro,-ide>,h<follo",nKto~"'IOfSIL.

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Anc\'cnllreeisagraphicallogicmodellhatidcnliticsandquanliticspossibleoulcomcs following3f1initiating event [Gbodrau etc.,2007).Event trees begin with an initiating event andworktowarda finalresult Thi approachis inductive.Themcthod provides information onbowa failure can occurand thc probability of occurrence.When an accidcntoccursina plant. \'arioussafcl)'sytCTJ1comcintoplaytoprevCDtthcaccidcnl frompropagating.Thescsafcty) tC'1T1scilherfailo rsuccecd.lbeeventlreeapproach includesthe effects ofanevent initiation followedbythc impact of the safety system

The typica lstepsinanCVC'n ltree analysis arc:

I.ldcntify an initiatingeventof intcrest.

2.Identifythesafety functionsdesigned todealwiththeinitiatingevent, 3.Con structthe event tree,and

4.Describe the rcsultingaccidcnt cvcnlscqucnces.

lfnppropriatcdataarcavailable,theprocedure isused10assignnumcricnlvnlucstothc variousevents.Thisis usedeffectivelyto determinethe probabilityof a certainseqlienee of cvcntsand todecidewhat improvements arcrequired.Anexampleof eventtree analysisis shownin Fig.7.

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S13titic isamathcmaticalsc icnCC'pcrL1iningt o thec o llcction,analysis.inteTprt'tat ion or explanation.and presentationofda13(MoscsC'tc.•196).StatisticalPnxessContr ol is defincdasasytern that uSC'S statisticstoidentify special ca uses cfvariationina proces (Lro nard, I996).StatiSlicaI Proccss Control( SPC) ",as pionccn:d by \\' alter A.hcwhart in the earlyI920s.W.Edwards Dcming lalcrapplicdSPCmethodsintheUnited States duringWorldWarII.therebysuccsfully improvingqualityinthe manufacture of munitionsand other strategicallyimportant products.Demingwas alsoinstrumental in introducing SPC methods toJapanese industryafter the war had ended.In 1989, the SoftwareEnginee ring Institute introduced the notionthat Sl'C can be usefully applicd to non-manu facturin gproce sses.suchassoftware engineeringprocesses.Through surveys and researches,Ideeplybelievethat SPC method can be appliedin thcproccss industry, that is,10be utilizedinmy dcvclopmcnt forth cfaultdiagnosis and rcaltimcmonitorin g oftheprecess system.

Statistica lProccss Control (SPC)is:ll1cfTcctivemclhod o f monitoring aprocessthrough theuse of controlcharts.Contro lchartscnablcuhcusc of objcctivecritcria for distinguishing background variationfromeventsofsignificancebased onstatistical techniques.Much of itspowerIies in the ability to monitor both process center andiIs variation or deviationaboutthatcenter.Bycollecting data over time atV3riOUSpoints within the process.variationsor deviation sin the proces scanbedetectedandclearly displa)'t'd.lfthede\'iationC'xcecdsth~holdspredefincd,thenafaultprobablyocc:urs.ln this research. SPC"illbeused as a rauhdiagnosimethod topcrfonnfaultdiagnosi functionto the process systems

A control chart isa statisticaltool used to distinguish between variation in a process resultingfromcommonC3USCSand variationresulting from special causes.llprescntsa graphiedisplayo f proccssstabilityor instability overtime.

E\'cryproeesshasvariation.SomcYariationmaybc thcresuhofeauses whicharc not normallypresent in the process.This could be special cause variation.Somevariationis simplytheresult of numerous, ever-presentdifferences in the process.This is common cause variation. Control Charts differentiatebetweenthcsetwo rypcs of variation.

In gcncraf. conuot chan containsa ccmcr Iinc that represents themean value for the in-comrol process.TwoOlherhorizontallines.callcdlhcuppcrconuollimit(UCL)and the lowercontrollimit(LCL),arcalsoshowninFig.8.These controlIimits arc chosenso that almostallofthe datapoints ...illfall within these limits as longas theprocess rcmainsin-eontrol.lfa singlequ ality charactcristich asb ecnm casured orcomputedfrom a sample, thecontrolchartshowsthe valueofthequality charactcristic versusthe sample

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The purpose in adding warning limits or subdividing the control chanintozonesis to provideearly notification if somethingis amiss.Instead ofimmediatclylaunching a proccssimprO\·ementetTontodetennine whcthcrspccialcauscsare prcscnt.thequality cnginccr may temporarilyinc reasethcratcatwhichsamplcsa rc takenfro mthc process output untilit's clearthattbc prcccss istruly incontrol.

Oncgoalof usingaControlChanistoachieveandmaintainprocessstability.Process stabilityis defined as a state inwhich aprocess has displayed a certain degree of consistency in thcpast and is expected to continueto do so in the futurc.Tbisconsistency is characterizedby a stream of data fallingwuhin control limits based on plus or minus 3 standard deviations(3 sigma) oflhecCnlcctine( \\'hcclerandChambcrs.1992)

Instatistics,signal processing and financialmathematics, alimc series is a sequenceof data points,measuredtypically atsuccessivelimesspaced at unifonntime intervals Examplesoftimeseries arc thedailyclosingvalueofthe Dowlonesindex ortheannual nowvolumeof the NileRiver atAswan.Timeseriesanalysis comprisesmethodsfor analyzing time series data inorder10extractmeaningful statisticsandother characteristicsofthe data. Time seriesforecastingis the usc ofamodclto forecastfuture events based onknownpast events:to predictdata points bcforc thcyaremeasured.An exampleoftime series forccastingincconometricsisprcdictingtheopeningpriceofa stockbascdon itspast performance.

An example oftimc series for randomdata plus trend.with best-fit line and diffcrent smoothing is shown in Fig.9.

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Expanding out EMAynlmiayC3Chlime results in the fcll owin gpower scriesvshowing how lhcwcightingfacloroncachdalapointpl.p2.ctc.dccrcasecxponcnt ially:

E.lIA=a.(p,+(I-a)p,+(1- a )'p, +(1- a )'p,+...)

SMAtcchniqucis intuitive and simple.CMAtcchniqucis not3Sintuitiveandsimplcas SMA,butit ismore efficientin detectingsmall shifts.EWMAtcchniqucisu scdfor dctcctingsmallsbifts.TikeIl.Srr102(1in the processmean.

I..lObjecti.-esof tbis Research

As process industrial systemsbecome larger and more complex, the total amount of energy andmaterial beinghand led increases.making fault diagnosisand safety management considerablyimporta ntboth fromtheviewpointof processsafcry aswell as cconomic loss.Thc rc exislvarious kindsof mcthods lodolhcfault d iagnosisandsafety managementtothe industrialprocesses.However,due to the limitations in various mcthods,thccffcclSfor faultdiagnosis and safety management arc nor that desirable.For this rcason,Vcnkatasubramanian C1C, (:!OO3)even proposed to develophybrid systernsto O\'CrCOffiCthe limitationsofindi\idual solutionstrategies.

Motivatedby the desireofsccking an effective approachtopcrform faultdiagnosis and implemen tsafety managementin process systems,andbytheCUTTCnlsiruation for solving thisprob lcm in academ ia, an innovativc mcthodology of risk-bascdSPC fault d iagnosis and its integra tion with SafetyInstru mented Systemisproposcd in thisthcsis.To verify thismcthod olob'Y,G2dcvclopmcnt so fiwarc fromGcnsymCorpora lioni5utilized inthis

• Topropose an innova tivemethodologyof risk-basedSPCfault diagnosis and its integra tionwithSISto solve the fauhdiagnosisand safetymanagemcnt prcblcm in processengineering

• Using G2 developmentenvironment. to implement andverifythe proposed methodology in a tank filling system developed with G2 softwarc.

• Realizing atechnique breakthrough, from univariate control to multivariate

• Simulatinga real process system, the steam powerplant system. in G2 devclopment environme nt,totestifytheproposedmethodology.

1.5 Orga niza tionof thisThesis

Sixchapters arc includedin this thesis.In ChapterI, the knowledge of SIS.safety analysis andstatistical process controJarc introduced.Theobjectives of this research arc also prcscntcdi nthischaplcr.lnC haptcr2,thccxistingfauhdiagnosis methods arcfirst reviewed.Then, aninnovative methodology of faultdiagnosis andsafetymanagcment fcr process systemis proposedandverified theoretically.Atlast,the G2development environmen tis introduced. In Chaptcr3, thcproposcdmethodology isimplcmenledand verified intheG2 development environment through developing atank fiJlingsyslcm.

Meanwhile, thcproposcdmethodology is testifiedthatit neither depends on any model . nordepends on largehistoricaJ data.To demonstrate the advantagesof the preposed mClhodology,3comparisonbctwccn thc tankfillingsystcmdevelopcdwiththcproposed mcthodologyand a traditional design for the same system is held.In Chaptcr 4,thc proposedmethodology is furtherimplemen tedand verified inthcG2development environment throughdevelopi ng another process system.the steampower plant system In themeantime ,a techniqu ebreakth rough ismadein this chapter.At the endorthis chapter.a comparisonbetweenthe steam powerplant systemdevelop edwiththe proposedmethodo logyandthe traditionalexpertsystemsmethodfor thesamesystem is held.InChapter 5,the tencharacteristicsof theproposedmethodologyarclisted.In Chapter6,conclusion forthisproposedmethodologyismade. and the future works for this researcharcpresented

Cha pter 2 l\lethodology ofRisk-based SPC Fault Dia gnosis andSafetyl\l an agem entforProeess Syst em

2.1 Rc,'iewuf E,i stin g F:lUltlli al:nusisi\! cth ods

In the areaofprocess fauhdiagnosis,thctcnn fault isgcnerallydcfincd as a departure from an acceptable range of an ObSCT\'cdvariable or a calculated parameter associated with3process(Himmclblau.1978).This defines a fault as a process abnonnalityor symptom,such as high temperature in a reactor or low product quality and soon.The undcrlingcause(s)ofthisabnonnality.suchasafailed coolant pump0ra controller,is (arcj called the basic evenqs) or the root causc{s).The basic event is also referred to as a malfunctionora faiJurc.Early detection and diagnosisof processfaults while the plant is stillopcratinginacontrollablc rcgioncanhclpavoidabnonnalc\'cnt progrcssionand redu ceprod ucti vityloss

From amodclingpcrspccuvc.uhcrc aremethodsthatrequireaccurate processmodels, semi-quantitative models. orqualitativemodels.On theotherhand.fhcrcarcmcthodsthat don ol assumc an yfonn o fm odcl infonn ation andr cly only onhi storicaIprocess data.We broadly classifyfaultdiagnosismethodsintothreegeneralC3tCgOries.They arc quanti tativcmodel-basoo mcthods,qualilativemodcl.bascdmcthods,andprocess history based methods (Venkatasubramanianctal..2003).Thcclassification of fault diagnosis methodsarc shownin Fig.13.

There are abundant literatures on process fault diagnosis approac hcs whichrangefrom analytical redundancyto knowledge-ba sed systemsand neu ral netwo rks.Ghetieet al (1998 ) propose a fault diagnosis approach usingbalance equationsmethodsandthe algorithmic redundancy.In this approac h, they illustratethe algorithmicredunda ncy concept usingtwo representativefau ltdetect ionandisolation methods based onbalance equat ions.An approach of modc1-based fault diagnosis using knowledgebase andfuzzy logic techniqueispresentedbyMohamed et al.(2002).Theinput/o utput measurements are usedto generate analytic symptoms. IIcuristic sympto msobse rved by theoperatoror based onthe processhistory areanothersource for fault diagnosis.Lo cr al. (2006) develop an intelli gent supervisoryeoordinator (lSC)fotproccss supervisionandfault diagno sisin dynami cphysical systems.Aqualitativebond graph modeling scheme, integratingartifi cial-inteIIigencet echn iques withc ontrolengineering, isu sedt o construet theknowled gebase oftheISC.Themodeltypewhich theanalyticalapproac hescan handleislimitedtolinear, andinsomecases,to very specific nonlinearmodels.For a generalnonlinearmodel,linear approxi mationscan provetobepoor and hencethe effectivenessofthesemethodsmightbe greatlyredu ced.Model-basedfaultdiagnosis requires accura te processmodel s. while the computationalcomplexit yinreal-time fault diagnostic systemsandthe difficulty indeveloping accurate processmodelsmakethis approach impracticalinrealindustrial processes.AlbazzazandWang (2004)propose a monitoring andfaultdiagnosis method forproeess by derivingS PCeharts basedon lCA (lndepend ent Component Analysis).He et al. (2006)presen t anoveI process fault detectionand diagnosistechniquebased on principalcomponen tanalysis (PCA).The proposedmethod reduces thedimensionality of theoriginaldata sctbyth eprojection of thedata setontoa smallersubspace definedbythe prineipaleomponents throughPCA.A major limitation of PCA-basedmoni toringisthat the PCAm odelistimeinvariant, while most of the realprocesses aretime-varyi ng. HencethePCAmodel shouldalsobe recursively updated.Simani and Fan tuzzi (2000) propose a FDI(Faull Diagnosis and Identification)method ology.ThisFDI methodology consistsoftwo stages.In the first stage,thefaultisdetected onthe basis of residuals generatedfrom a bank ofKalman filters;inthe secondstage,faultidentificat ion is obta ined frompattern recognition

tcchniq ucsimplcmcnt cdbyNcuralNctworks.To cnh ancc faultdiagnosis reli ability,

espec iallywhen thedata available fortrain ingthe netw ork arcnotabundant

whenreasoningwithqualitativemodels.Fromindust rialapplicationviewpoint,

single method has all lhe desim blejeo lll1l!Sone ....'OlIld /ikeodiagnosticsys lf:m topoS.H!.'U Ilis ollr ~·iewlhal some ojlhesemethocls C'on C'omplemenl one anolher1l!sulling inbetter diugnostic systems.Integratingthese complementaryfeatures is onewayto develop hybr id S}'slemslhal C'ollld o\'f!rrometh e/imii alions o!indi\'idual soIlIlionslr alegies."

In this situationfor fault diagno sisin proccsscnginccrin gandthe aforementioned(in

ChaptcrJ}safctyincidcntshappcncd inproccss industricsthat leadtothe serious consequences forpeople,the environment and propert y,itis impo rtant andirnpcrativcIor our researchersto find an effectivemethodtopcrfonnthefault diagnosis andsafety management10the process system.These factorsmotivated theproposalofan innovativc methodology o fri sk-bascdSPCfault diagnosisandits imcgra rionwithSIS forprocess

2.2 1'roposed ;\le thodo logy

Since therearc various bewildering fault diagnosis approac hesinprocessengineering.

and forthe existingmethods,quantitative model-basedmethods,qualitativemodel-based mcthodsund processhistory based methods,eachof themhasitslimitations,itisnot an idealsolution forustofollow one branchintheclass ificationofdiagnostic algorithms showninFig.l3,nort hchybridsystemssolution proposedbyVcnkatasubramanianctaI..

Statisticalapproachis easy10build andit performs considcrablywcllonfastdetectio nof abnormalsituations,andithasbeen successfullyimplementedinindustrialapplications.

butitbclongstothe conven tional processhistorybased mcthod,that meansit needs a largcamounto f historical proccssdala.lf wccutthcdcpcndcncc bctwccn statistical and a largemoun t ofhistorical processdatawhicharc rcqu ircd by thcconvcntionalproccss historybasedmethod in Fig.13.and wedo notusc any branches below statistical mcthod.

i.c.•PCAlPLS orStatistical Classi fiers,thenthisbrand new approac h isdesired10be an idealsolutionfor thisprocessfaultdiagnosisproblem , because it willneitherdepend ona large amount ofhistoricalproce ssdata,norhavethelimitat ionsfromPCAIPLSor StatisticalClassifiers methods.Bused onthesethoughIS,aninnovativemethodology, risk-basedStatistical Process Control(SPC)fault diagnosisand itsintegralionwithSIS forprocess system.hasbeen proposed .Thcpathway of theproposed approaehforfault diagnosisintheclassification of diagnostic algorithmsis shown inFig.14

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In order10theoreticallyverifythe proposcd risk-based SPC faultdiagnosi s mcthodology, historicaldata from Thermod ynam ics andFluidsLab inFacultyof Enginecring and AppliedScience at Memorial UniversityofNew foundlandwill bcused in this analysis.

Thesehistoric aldata arc thesteam pressures ofthe stea mpower plant in the Thermodyna mics andFluidsLab.Historical data obtai nedduring12:49p.m.through 12:58p.m.onJuly 13,2006 arc takento doIhc \'erific3tion.Thc stcam prcssurc dala in normal operationarcshown in Table 4(norma l situation ).A fault event issimulated in thislimcperiod,andthccoITCsponding,dJtaarcshowninTablc5 (abnormalsirualion},

Table~:Slum PressureDatarOTtheStramPowerPlant(Ne r ma l Situation )

T:lhlcS:Slea mllress u re n a la for t hcS te llm l' owe rI1hlUl(A bnu r ma lS il ua tio l1)

In this risk-basedSPC fault diagnosis methodology ,movingavcrage technique wil!be utilized. To increasethe sensitivi tyof the risk-based SPC fault diagnosis mcthod10 1hc faultevent.the numberof datapoints, 3,is chosen to do the movingavera ge calculaticn.

Thesteampressuredataobtained fornonnal situation and abnorma lsimation arc shown

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I,Normality TesttotheMovingAverageStearnPressureData

Inorder10testifthemoving average steam pressuredataarcnonn allydistributcdcthc norm alitytests in MinitabIS arc condu cted.Theresultsarc shown in Fig.17 and Fig

Fig.17is the normalitytestfor themovingaverage steam pressuredatain the Steam PowerPlantSystemin normal operation.FromFig.17,we can secthat:TheP-Valuc

>O.IOO(that is.P-Valuc>o.0 5);RJ::-O.990, is,"cry cJosc to I.So thcmo\·(ng avcragc stcam

prcssurcdata aren onn allyd istribulCd.

1600 62D 640 660 680 100 720

Fig.18isthe nonnalitytcst[or the moving averagesteam pressuredataintheSteam PowcrPl ant Syslcmin abnonnalsitu3tion,From Fig.18, wc cansceth at: ThcPvvaluc>

0.100>0.05; RJ=O.981.isveryclose toI.Sothemovingaverage steampressuredata arc

If theprocess is in normal operation. according to the three-sigma rule,the moving average steam pressure data points should fall into theILCl.Vel].i.e.,[590lPa.

690kPa];otherwise,there couldbea fault event.Plotting the moving average steam prC'SsurcdatainExcel2003,thefollowingresultsareobtaincd.assho\\ninFig.19 and

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Fromthc:abo\·cchart..wccansccthatthcsixthdalapoinlfallsoutsidcthe[590,690].so theprocess is suspected to be abnormal,i.e..there couldbea fault.

2.3.3Ri,k-basedSPCF.ult Dlagnesls

To minimize the number of false alarm ,risk or risk indicator concept isintroduced into the proposed faultdiagnosismcthodologylOidcntifyanddctcrminepotcntial fault() Risk is estimated for each deviation in the predicted values of control variables • using probability oflbe deviation and ilSassociatcdscvC'rily.ThC'probabilit)'ofthefaultis assessedusing three-sigmarule.whereasthe severity is assessed using the dcviatio nfrom thcpredcfined thresbold valuets]

Accordi ng10the defin itiontothcproccssnsk.thcculculauonoftheriskofafault inthis research is nsfollow s.

P(F)istheprobability offault.P(F)=;(·' -(J~+3U)1 Sislhe~'"Verityoffault.5=100''''·

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detectthisdeviationin advanee,andevaluate itsrisk,lhen takeeorres pondi ngaetion(s) promptly.After implement ing theproposed strategyofSIS 1andSIS2,theSafety IntegrityLevel (SIL)of thesafety systemhasupgraded fromSILltoSIL3.

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•Monitoring. diagnosis, and alarm handling.

•Supcrvisory and edvanccd controt.

•Process design, simulation. and re-enginccring.

•Dccisions upponforcntcrprise-widcopcra tions.

G2devclopmentenvironmcntisagraphicalcnvironmcnt.Almostcycrything in G2 has a graphicalrepresentation.Thesystcm-defincddisplayilcmsinG2canshowthcstatcof the app lica tionas it changes overtimc, and the system-definedbuttons canbeused to send commands to G2 orthe outside world.Besides, G2 uses a structured natural language inprogramming statements.The G2 language is similar to ordinary human language,solhea pplicationdc\'clopmentprogrammedwithG2languagciseasierto read

Gz offcrs GatcweySrandardInterfacc (GSI)networkand intcrfacingeapability.ThcG2 GatewayStandard Interface (GSI)is anetwor k-orien tedtoolkitused for developing software inter faces, orbridges,betweenG2 andother,externalsystems.G2 Gateway allows Klfsto cxcbangc various types of databetweenaG2 processandthebrid gc.

GDA,theG2DiagnosticA ssistant, is ai aycredprodu clbuih ontop ofG2.GDAi s a visual programming cnvironmcnlfo rd cvelopingin tclligcntapplica lions that monitorand control real-timeprocesses. AGDAapplicationcontainsschematie diagramsthat

•Acquircdatafromreal -timeproccsses.

•Make inferences based on the data.

•Take actions based on thc inference valuC5,such as raising alarms, sendingmcssages

The principalcomponent of the GOA is a graphical language matlets you express complexdiagnostic procedures as a diagram ofblocks,also callcdanInformation Flow Diagram(IFD).Thcscbl ocks arc conncelcdb yp aths that show how datatl ows through

GUIDE.Ihc G2 Graphical UserInterfaceDevelopmentEnvironmenl,isadc:vclopmcnt toolthat enablcs uscrstocrc31cgraphicaluscrintcrfaces(GU I's)forG 2applications.A G2 GUIDEuserinterfacecanbeconstructed byusing thegraphicalcomponcntscalled UIL (Userlnlerface Library)co nlrols.GUIDElUIL providcsanapplication programmer's inlerface (API) 10procedu resthat control dialogsand otherclementsofagraphicaluscr inlcrfacc.GUIDEsu pponsditTerentciasscsofU ILcontro lsfordiITcrentpurposes'

• Some classes ofUILcontrols, such asedit bcxcs.Bunons.and scroll areas.cnab lc users10 view andeditthedatastoredin object annbutcs.TheditTerent classes are suitablefo rvicwingandcditingditTercnttypcsofdata.

• OtherciasscsofUI Lconlro ls,such as bordersand separators,enable users to organize a userinterfacevisually.

In thisresearch.integ rated G2developm entenvironment,i.c.,the integration ofG2 &.

GOA&GUIDE,isusediodevelop3pplieationsyslcms inciudingthc Tank Filling

Systemand the Steam PowerPlant System,andisalsoused10 verifytheproposed methodology ofrisk-basedSPCfaultdiagnosis andsafely managementfor process system.Recentl y,mostfault diagnosisusingG2 softwareemploy the expert system approac h.Todemonstra tethe advantagesofthe proposed methodology overexpert system,a eomparisonwillbeheld between thesetwo approachesin Chapter 4.

Chapter 3 Implementation and Verifi eationof the Proposed Methodologyin G2 Developm ent Env iro nme nt-

Tank Filling System

In ordcr to testify thc proposcd mcthodology of risk-bascd SPC fault d iagnosisand safcty managementforprocess system,from this chapter10next chapter,twoprocess systems arebuihinG2dcvelopmcntcnvironmcnt.Thcfirstprocl."Sssystcmisatankfillingsystcm.

atanklevel monitor.in process industry,aswillbedescribed and studied in this chaptcr.

Thcsccondprocess system isa steam power plant systemlocatedin Thermodynamics andFluidslab inFaculty of Enginccringand AppliedScience buildingatMemorial UnivcrsityofNcwfoundland,3swi lllx dcscribcdandsludicdinChaptcr ·t

In this chapter, theproposed method ology is imple me ntedandverifiedinthe G2 developm ent environ mentthroughdevelopin gatankfillingsystem,Meanwhile.the proposedmethodology istestif iedthaiit neither depends on anymodcl, nor dcpends on largehistoricaldata.At thecndofthischaptcr,lodemonstralcthe advantagesof'the proposcdmcthodology,a comp arisollb clwccn thcl ank filling syslcmdcvclopcdwilhlh e proposcdmclhodology and at radit ional dcsign rorthc samc systemisheld.

3.1 Requ ir ements10the Tan kFillin~ S)'sI Cll1

In this chapter,3tan k lillingsystcm, i.c.,a tank level monitor,istc bc dcvclopedin G2 developmentenvironmen t.In this system. tankis filled with inflow liquid through a manualvalve.Thebasic process control tothis filling system is tomaintainthecontrollcd variable,the tank level,atsomedesired value, i.c.,the set point.Ifsome disturbance causesthe tank level deviateaway from itssetpoint,someprotection laycrs,i.c.,safety instrumented systems,shouldbeaddedintothis system to ensuresystcm safcry.Thetank filling systcm10bcdeveloped should havc thefollowingfunctions:

•Poppingupwamingmessagewhenranklevelrcachessomelimit;

•Raisingalannwhcntanklevclexeecdsuppcrconrrollimit;

•Raising alarmwhen there is a fault and then shut down the system;

•Raising alarmand shutdown the system immediately whenthere is an excessive

Accordingtothe requirementsto the tank filling system tobedeveloped,three developmentstages willbeconducted and studied in three subsequent sections.These three developmentstages arc deterministic stage, SPCstage and risk-basedSPC stage.

In deterministic stage, the console forthcunk filling system containing basicprocess control system BPCS,protection laycrSISIand protection layerSIS2is built in G2 developmentenvironment, and the basic functions forLhis fillingsyste m are provided In SPCstage,statisticaltechnique of moving average isused to filter outthe noise disturbances.Statistica l tech nique of control chartis uscdto momtor thctankl cvcli nthc wholeprocess of the tankfilling system. Bcsidcs,t ocn sure an cvcntis a fault,thefaultis definedasthreesuccessivedata points of tanklevel cxcccdthcuppercontrollimil6m .ln risk-based SPCstage,risk indicator isintroducedintothemethodology10reducethe numbcrof falsc ularms.real timernonitor ingtothe process isperf ormed ,andforecast function tothefaultevent is conducted

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In the dctcnninisticdcvelopmcntSI.1gc.onlyaset of detcrrninistic results canbeobtained. there arc not noise filtering technique applied to Ute fillingsystem to Iiltcr out noise disturbances which could lead10false alarms,and the developedfault diagnosis function cannolpcrformrcaltimcmonitoringtothcwholcproccss.lna ddition.the determination of the faultis thatone data point of tank level exceeds the upperlimit 6 m,thcn the actionslike raising alannand shutting downthe systemwillbetaken,which willincrcasc the probability of false alarms. Therefore,SPC fault diagnosisand safety management (SIS)&SIS2)method is introduced10overcome the aforementioned disadvarua gcs in deterministic development stage.

In SPC dcvclopmcnt stagc. statistical tcchniquc o f moving a\'cragc is used to filtcr out the noise disturbances.Statisticaltcchniquc o f control chan is used to monitorthe tank level inthewholeprocess ofthetank filling system.Besides, using control chart and three-sigmarule.ifthrcesuccessive data pointsof tanklevel excccd thcuppcrlimit am, thenthisisdefinedasafault event.

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Although inSPCdevelopmentstage,thedeveloped faultdiagnosisandsafety managcmen[ syslcm ovcrcomcs thcdisadvantagcs cxistingindctcnninistic stage, thereis notforecastcapability inthc SPC fault diagnosis andsafetymanagcmcntstagc.Forccasl capability is a very importantcharacteristicforafault diagnosis andsafety management system.Using forecastcapability,potential risks of theindustrialprocesses can be identifiedandcoITcctcd,thus itcanreduce thehazards 10people,property and environment.Another drawback inSPC stageis thenumber of thealarms is stillhigh Besides,inSPC stage,realtimemonitoringfunctionhasnotbeenimplemented

Inordertominimizethenumbcrof alanns,performrealtime monitoringto theprocesses andconductforecast function10the faultevent,themethodology of risk-based SPCfault diagnosis anditsintegrationwithsafetyinstrumented systemSlSI&SIS2isintroduced inthis stage.Thedevelopedconsoleforthetankfilling systemis showninFig.29.Since thisrisk-basedSPCfaultdiagnosisandits integrationwithsafety instrumentedsystem

SISl&SIS2 isthefinalizedproposedmethodology,itwill bedescribedindetail as

below

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Topcrfonn the bestlinear trend forecastthe previousthree data points are used10dothe bestfitforaline,so we canobtainthebestfit valuefor thethird pointand the rare of change,i.c.,the slope,ofthe bestfitline.Withthis besttit line.weCan predict the value ofnext (fourth) data point.Thedata pointsof tanklevelmoving average andthei r forecasted datapointsarcshown in Fig.31.Since whatwe conccminthisfill ing syslcm iswhetherthetank levelexceed sthe upperlimil, onlyth cuppcr ccntrcllimitisdrawn in