To be published in the Journal of Intelligent & Robotic Systems, Kluwer Acadelic Publishers, 2004

Transcription

1 To b publishd in h Journal of Inllign & Roboic Sysms, Kluwr Acadlic Publishrs, 24 Th Discr Evn Concp as a Paradigm for h Prcpion Basd Diagnosis of Sachm Marc LE GOC 1&2 and Claudia FRYDMAN 1 1 LSIS Domain Univrsiair S Jérôm, Avnu Escadrill Normandi-Nimn Marsill Cdx 2, Franc {marc.lgoc, Claudia.frydman}@lsis.org 2 Sachm Consuling Sollac Médirrané Bâ DB Fos sur Mr Cdx, Franc marc.lgoc@ixis.arclor.com Absrac: Sachm is an xnsiv larg-scal ral im knowldg basd sysm dsignd o monior and diagnos blas furnacs. This papr aims a illusraing h way h concp of discr vn allowd h dfiniion of a prcpion basd diagnosis approach as a rcursiv and holographic absracion procss of discr vn. Th xampl of h diagnosis of a hrmal load phnomnon on a blas furnac is usd in ordr o illusra h way Sachm apply h prcpion basd diagnosis approach. Som considraions abou h blas furnac and h dvlopmn of Sachm ar also prsnd in h papr o rcall h complxiy and h issu of h dsign of powrful prcpion sysms. Kywords: Faul Diagnosis, Moniord Conrol Sysms, Knowldg Basd Sysms, Discr Evn Sysms, Arificial Inllignc. Cagoris: (1), (5) 1 INTRODUCTION Sachm, an xnsiv larg-scal ral-im knowldg basd sysm dsignd o monior and o diagnos dynamic procsss, is an xclln xampl of Arificial Inllignc usd o h mans of indusrial and conomic succss. Sachm was iniially dvlopd o monior and o diagnos h blas furnacs of Usinor, a company in h Arclor group, h world s largs producr of sl producs. Th aim of Usinor was o sav up o 1 pr on of pig iron (s L Goc and Thirion, 1999 for a prsnaion of h projc). Currnly, Sachm arns bwn 1,5 and 1.7 pr on of pig iron. Wih six blas furnacs quippd wih a Sachm sysm, Arclor arns bwn 16,5 and 18,5 millions uros pr yar. Sachm works in ral im and coninuously (i.. 24 hours pr day, 365 days pr yar), wih an availabiliy raio quals o 99,7%. Th main ask of Sachm is o rason in im and in spac in ordr o drmin h sa of h blas furnac from h snsor daa providd by h insrumnaion (Figur 1). On h main difficuly whn dsigning such a sysm is h acquisiion and h rprsnaion of h prcpion knowldg of xprs ha monior and diagnos h blas furnac. Th main propris of h prcpion knowldg ar (i) o b undr-symbolic and (ii) concrnd wih mporal and spaial voluions of h procss. Th firs propry mans ha an xpr canno us words in ordr o dscrib is own cogniiv procss of prcpion. Th scond propry mans ha humans hav difficulis whn dscribing prcisly h voluions wihin a sysm: i is asir o spak abou h saic propris han h dynamic propris. I is hrfor vry difficul o modl h rasoning of a conrol procss xpr. Th arificial inllignc approach sumbls ovr his difficuly. Inpu Mssags (Procss Daa) ~11 daa/min Dsird Procss Bhavior Acions Prcpion Diagnos Corrc Causs & Problms Acions Procss Phnomnon Warnings Sachm Monioring DB Communica Figur 1: Sachm Sysm in Opraion Oupu Mssags (Alarms & Rcommndd Acions) ~7 mssags/day This papr aims o illusra h way h noion of discr vn allowd h dsign of h prcpion funcion of Sachm and is global rasoning procss. Th nx scion inroducs brifly h problmaic of h blas furnac conrol. This prsnaion allows in scion 3 o analyz h srucur of h knowldg rquird wihin a procss conrol ask. This analysis shows ha h discr vn concp is a basic lmn for rprsning h procss conrol knowldg for blas furnacs. In scion 4, h

2 principls of h prcpion funcion of Sachm ar dscribd. Scion 5 aims a dscribing h main characrisics of Sachm. This scion provids also som quaniis ha hlp in valuaing h scop of Sachm and h dvlopmn projc. Th ral xampl of h dcion of a scaffolding phnomnon is usd in scion 5 in ordr o dscrib h prcpion funcion of Sachm. Th papr concluds hn on h conomic prformancs of Sachm and inroducs our currns dvlopmns for h nx gnraion of Sachm sysms. 2 BLAST FURNACE Each of h wo Fos sur Mr blas furnacs (Figur 2) rprsns an invsmn of 76 million uros for h firs consrucion. A blas furnac is r-consrucd vry ~15 yars (~15 million uros). Th firs is h complxiy of h physical and chmical inracion phnomna bwn gas, solids and liquids. Ths inracions govrn h bhavior of a blas furnac and up o now, no mahmaical modl xiss for dscribing hs inracions in a whol. Th scond coms from o h xrm condiions ha ruls insid h blas furnac: vry high mpraurs, acidiy, dus, high prssur, c. Wih such opraional condiions, i is indusrially impossibl o mainain snsors insid h blas furnac load. As a rsul, h housands of insrumnaion snsors ar posiiond around h blas furnac sack and no inrnal variabl of h load is accssibl o b masurd. Th lack of mahmaical modls for h blas furnac dynamic ld o h impossibiliy of applying h horis of h conrol procss. 2.1 Blas Furnac Procss A blas furnac is a srucur of ~1 mrs high ha conains a chmical and hrmal gas-solid counr-currn racor of ~3, m3 in ~27 mrs high (Figur 3). Th inrnal prssur is ~3 absolu bars and h powr insid h racor is ~1,3 mgawas. Such a blas furnac producs around 6, ons of ho mal pr day. Figur 2: Blas Furnacs a Fos sur Mr A blas furnac is loadd a h op (hroa) wih or and cok (i.. mor han 95% of carbon). Ho blas pips ar usd o injc ho air wih addiions of oxygn and pulvrizd coal. Th oxygn burns h carbon conaind in h cok and a flam of ~22 c is crad. This combusion producs a gas (CO) ha rducs h ors (f x O y ). Th ha and h rducion gas gnra a flow of liquid iron and slag ha cass down o h blas furnac crucibl. Thr, h liquid iron chargs wih carbon o form h pig iron, which is xracd hrough a hol. Slag and pig iron is hn sparad by dcanaion, and pig iron is hn carrid o h slworks wih ladl cars. 2.2 Spcificiis of Blas Furnac On of h main spcificiis of blas furnac is h lack of mahmaical modl of is inrnal dynamic. Thr ar a las wo main rasons: Figur 3: Blas Furnac Procss Anohr imporan spcificiy of blas furnac is h vry long larning priod (5-1 yars) ha is ncssary o larn o conrol h producion of blas furnac (1 prsons ar rquird pr worksaion). This is rlad o h lack of mahmaical modls for h blas furnac dynamic. Th conrol of h producion dpnds significanly on xprinc, ha is o say pracical skills and informal knowldg. An aging workforc and h subsqun loss of xprinc du o social and conomic condiions also affc his facor. Th lmns dscribd abov consiu h main incnivs o dsign h Sachm sysm hrough h mans of Arificial Inllignc chniqus. 3 KNOWLEDGE TO CONTROL Evry conrollr, human or sysm, prforms wo basic funcions (Figur 4): h prcpion of h procss sa and h corrcion of h unsaisfacory sas. Th sa of h procss is hn h pivo of h wo basic funcions of diagnosis and monioring sysm: h prcpion funcion aims a rcognizing h procss sa and h corrcion funcion dfins h adqua acions o achiv. Diagnosis is hn includd in h prcpion funcion in ordr o compl h dscripion of h procss sa.

3 Th goal of h conrollr is prcisly h disapparanc of h unsaisfacory sa(s): an acion is rquird whn h sa of h procss is no saisfacory, and ohrwis nohing has o b don. So, whn rquird, h conrollr mus dcid and achiv and acion on h procss. An acion is a modificaion of a las on of h inpu variabls of h procss. Th causal rlaions bwn h variabls will ransform and propaga a modificaion of an inpu variabl on h inrnal variabls and ulimaly on h oupu variabls. Dsird Bhavior Unsaisfacory Procss Acion Prciv Sas Corrc Procss Procss Daa Figur 4: Th Prcpion-Acion Closd Loop Th blas furnac xprs know a s of causal rlaions ha ar opraional a las during h xploiaion phass (i.. spcific causal rlaions ar rquird for saring up or swiching off h procss). This yp of causal knowldg prsns h following characrisics: A modificaion ha occurs insid h blas furnac is calld a phnomnon. A phnomnon has ffcs on signals. Th appariion of a phnomnon insid a blas furnac causs modificaions on signals. A paricular phnomnon causs a spcific s of modificaions on a spcific s of signals. This spcific s of modificaions is calld a signaur (hallmarks). Th dcion of h occurrnc of a phnomnon is achivd by h rcogniion of is ffcs on signals via an appropria s of signaurs. Gnrally, h xpr s knowldg rfrs o h usual ffcs of a modificaion (dircion, magniud and im inrval) of h valu of a variabl on anohr. Typically, h magniud of h ffc is valuad by h mans of sady sa modls. Som knowldg abou h conx can clarify h condiions for using a known causal rlaion. Ths condiions ar givn in rms of h absnc of ohr phnomnon ha inhibis or prvns som causal rlaions. 3.1 Causal Knowldg for Conrol To conrol h bhavior of a procss, on mus hn know h rlaions linking causs o ffcs. Th naural form of h xpr knowldg is h if-hn rul. Concpually, h simplr form of such a rlaion is: If h procss is in h sa X and if h acion U is achivd Thn h procss producs h oupu Y In his rul, h procss oupu vcor Y is h ffc of a modificaion of h inpu vcor U. This rlaion dpnds of h procss sa vcor X: h condiion for having h Y ffc from h U modificaion is h fac ha h procss is in h sa X (X, U and Y ar vcors of h ral numbr s, R). Th causal rlaion can b modld as a 3-upl of h form: C(Y, X, U) In ordr o clarify h ky rol of h sa vcor, his rlaion can b rwrin undr h form of wo binary causal rlaions: C(X, U) C(Y, X) Whr C(X, U) dnos a causal rlaion from U o X. Th conrol of a procss bhavior consiss in rvrsing h causal rlaions in ordr o rach a goal: If h goal G is o obain h oupu Y and if h procss is in h sa X Thn h acion U mus b achivd Th invrs rlaion inroducs a nw rm: h goal G of h conrollr. This invrs rlaion is hn a 4- upl of h form I(Y, U, X, G) so ha : I(Y, U, X, G) C(X, U) C(Y, X) Equal(Y, G) Ths causal rlaions ar proposiions abou sady sas of h procss rprsnd by vcor valus. Thy do no ingra h dynamic of h procss, ha is o say h way h procss is changing. In ordr o vok h chang, h proposiions mus connc modificaions of vcors (Vila, 1994). Th basic hypohsis usd o dsign Sachm is ha xpr s rason mainly dircly on sa ransiions rahr han on h sa of h procss. Bcaus sa ransiions can b rprsnd by discr vns (Ziglr, 1976), (Ziglr, 1984), (Giambiasi 1999), h knowldg o conrol h blas furnac can b rprsnd wih proposiions abou discr vns and mporal windows ha consrains h im of h vns. 3.2 Causal Knowldg abou Discr Evns Th discr vn form of a causal rlaion adds imd consrains bwn h vcor modificaions: If h procss is in h sa X a im and if h acion U is achivd a h sam im Thn h sa procss will ransi o a nw sa X a im (+ x ) such ha x [T x -,T x + ]. If h procss ransi from sa X o sa X a Thn h procss will produc h oupu Y a im (+ y ) such ha y [T y -,T y + ]. Th sa ransiion from X o X consius an vn and is dnod X. Th discr vn form of h causal rlaions is hn (Travé & Miln, 1997): C( X, U, [T x -,T x + ]) C( Y, X, [T y -,T y + ]) Mor gnrally, h form of h knowldg in ordr o conrol h blas furnac is h following: Whr: R( Ou, In, [T -,T + ])

4 R rprsns a rlaion Ou is h oupu vn In is h inpu vn [T -,T + ] is h imd consrain ha dfins h im window for obsrving h occurrnc of h oupu vn Ou afr h occurrnc of h inpu vn In : if h inpu vn occurs a im, h oupu vn would b obsrvabl in h inrval [+T -, +T + ]. This knowldg mans ha hr xis a rlaion R ha connc an oupu vn Ou wih an inpu vn In so ha whn h inpu vn occurs a im, h oupu vn would b obsrvd a im [+T -, +T + ] (Hanks & McDrmo, 1994). Th hypohical flavor of his knowldg aims a caching h complxiy of h blas furnac s bhavior: h rlaion can b obsrvd in gnral, bu hr ar spcific siuaions whr h rlaion canno b obsrvabl. In ohr rms, phnomna can inrac so ha h oupu vn can b unobsrvabl in spcifics circumsancs. 3.3 Knowldg Rprsnaion A rlaion R( Ou, In, [T -,T + ]) is usually brokn down ino: a logical rlaion R L ( Ou, In). a imd consrain R T (d( Ou), d( In)) whr d( Ou) and d( In) ar h vn ims of h vns Ou and In. Hr, h imd consrain mans simply: d( Ou) - d( In) [T -,T + ]). Such a rlaion can b rprsnd wih a graph (Figur 5). I is o no ha rasoning wih h wo yps of rlaion, causal and mporal, is currnly h on of h major difficulis whn dsigning a diagnosis sysm (Cauvin al, 1998). In [T-, T+] Ou Figur 5: Rlaion bwn Evns Bcaus h vns can b classifid in cagoris, h basic form of h Sachm knowldg is illusrad in Figur 6 and is calld a signaur : [1s, 12s] [1s, 36s] 1 : E1 2 : E2 3 : E3 4 : E4 [1s, 6s] Figur 6: Exampl of Signaur This figur xprsss ha a squnc of 4 vns {1, 2, 3, 4}, rspcivly of yp E1, E2, E3 and E4 ar h sign of a spcific modificaion iff: d(2) [d(1)+1s, d(1)+12s] d(3) > d(2) d(4) [d(3)+1s, d(3)+36s] d(4) [d(1)+1s, d(1)+6s] Th yps E1, E2, E3 and E4 dfin paricular valus of aribus of vn frams. Th frams dfin cagoris of vns undr h form of logical propris. In ohr words, h basic knowldg o conrol a blas furnac is rprsnd by a graph whr nods ar rsricion ovr vn frams and links ar imd consrains. A signaur is hn a graph of discr vns linkd wih mporal consrains. Signaurs ar usd in ordr o rprsn h condiions of prcpion of blas furnac phnomnon: a signaur dscribs h sar of a phnomnon and anohr on dscribs h nd of a phnomnon. In ordr o dc a phnomnon, h signaurs mus b opraionnalizd (i.. compild ) in xcuabl srucurs lik chronicls (Ghallab, 1996). Supplmnary knowldg is rquird o drmin h propris of h phnomnon lik h cagory, h localizaion or h magniud. Such knowldg is saic and is rprsnd by usual ruls of h firs ordr prdica calculus. 3.4 Knowldg Opraionnalizaion Chronicl Calculus is a framwork for rprsning and using mporal consrains nworks whr nods ar vns and arcs ar mporal consrains. A mporal consrain managr, including a parn maching mchanism, vrifis if h occurrnc of vns saisfis h mporal consrains. P c b a Evn(P:(b, c), ) Hold(P:a, ( 1, 2 )) 1 2 Figur 7: Evn and Hold Prdicas Chronicl Calculous rly on proposiional rifid logic formalism (Soham, 1987) whr h nvironmn is dscribd hrough domain aribus P:v whr P is h aribu and v is valu (Dousson, 1996). Two prdicas ar usd for mporally qualify proposiions (Figur 7): hold(p:v, ( 1, 2 ) rprsns prsisncy of h valu of a domain aribu P ovr an inrval [ 1, 2 ] wihou knowing whn his valu was rachd. vn(p:(v 1,v 2 ), ) is a im sampd insanc of vn parn corrsponding o an insananous chang of a domain aribu valu P. A chronicl modl rprsns a pic of h voluion of h world and is composd of four pars (Figur 8): a s of vns which rprsns h rlvan changs of h world for his chronicl, a s of assrions which is conx of h occurrncs of chronicl vns, a s of mporal consrains which rlas vns and assrions bwn hm, and a s of acions which will b procssd whn h chronicl is rcognizd. Th mporal consrains graph associad wih a chronicl (Figur 9) allows compuing h las consraind pah bwn ach coupl of vns ha will b usd during chronicl rcogniion procss.

5 Th rcogniion mhod is basd on a compl forcas of forhcoming vns prdicd by chronicl modl (Dousson al, 1993). A mporal window W is dfind for ach vn of h chronicl conaining h possibl occurrnc das for in a parial insanc of h chronicl. Th acual da of mus b consisn wih consrains and known das of h ohr vns of h parial insanc. Th chronicl rcogniion volvs whn a nw vn arrivs or whn im passs so ha mporal consrains bcom obsol. Chronicl SwichProblm { } // - Forhcoming chronicl vns vn (Transmission: (on, off), 1 ); vn (Transmission: (off, on), 2 ); vn (Componn1 : (?, ok), 3 ); vn (Componn2 : (?, ok), 4 ); vn (Componn3 : (?, ok), 5 ); // - Assrions (conx) hold(traffic:normal, ( 1, 6 )) // - Tmporal consrains 1 < 2 < 3 < 6 ; 2 < 4 < 6 ; 2 < 5 < 6 ; ( 2 1 ) in [, 18] ; ( 6 2 ) in [6, 12]; Whn rcognizd{ } rpor «Swich pb dcion» Figur 8: Exampl of Chronicl Modl Th chronicl calculus chniqu is mbddd in KOOL-94, h knowldg rprsnaion languag usd o implmn Sachm (s furhr). This languag ingras a mporal consrains managr drivd from h IxTT sysm (Dousson, 1996), which as also bn ingrad in h TIGER sysm (Miln al, 1994), (Aguilar al, 1994), (Travé and Miln, 1996, 1997). Ohr indusrial xampls of such chniqus can b found in (Labori and Krivin, 1997), (Brdill al, 1994) for AUSTRAL or (Cauvin al, 1998) for GASPAR Figur 9: Tmporal Consrains Graph of Chronicl (wihou mporal consrains) Th inrsd radr can rfr o (Vila, 1994) for a survy of mporal rasoning in arificial Inllignc and o (Dagu, 21) for an inroducion o h modl basd diagnosis hory and h rfrncs for h main diagnosic sysms. 4 SPATIAL DISCRETIZATION Th aim of h xprs knowldg is o ransform h masurmns (i.. ral numbrs) ino symbols (i.. naural numbrs) so ha spcific rasoning can b achivd. This ransformaion is calld quanizaion (Ziglr al, 2) and is concrnd wih h numbr o symbol or h quaniy o concp ransformaion x & +2 i Figur 1: Discr Evn Gnraion 13 For Sachm, a rcursiv discr vn gnraion procss ha consius an absracion procss ralizs his ransformaion. Th rcursiv procss sars a h signal lvl: h mporal voluions of h signals ar dscribd by a flow of discr vn. This flow of discr vn is hn inrprd according o paricular chronicls in ordr o rcogniz nw discr vns of mor absrac lvl. This scion illusras his discr vns absracion. 4.1 Discr Evn Flow Th discr vn gnraion mchanism is basd on a sa spac discrizaion (Figur 1). A s of saic or dynamic hrsholds is usd in ordr o dfin spac aras. Basically, an vn is gnrad whn a signal crosss a hrshold ha is o say whn h procss mov from on spac ara o anohr spac ara ( 1,, 1) ( x =?, = 1) ( 2, x, 1) ( x = 1, = 1) ( 3, x, 2) ( x = 2, = 1) ( 4,, ) ( x = 2, = ) ( 5,, 1) ( x = 2, = 1) ( 6, x, 1) ( x = 1, = 1) ( 7,, ) ( x = 1, = ) ( 8,, 1) ( x = 1, = 1) ( 9,, 2) ( x = 1, = 2) ( 1, x, ) ( x =, = 2) ( 11, x, 1) ( x = 1, = 2) ( 12,, 1) ( x = 1, = 1) (,, ) ( x = 1, = ) 13 Figur 11: Discr Evn Flow In h xampl of Figur 1, 4 hrsholds (-1, -2, +1 and +2) ar dfind for a variabl (idm for is drivaiv & ). This dfins 5 rangs calld -2, -1,, +1 and +2. Whnvr i k (or i k ) crosss a hrshold, an vn is gnrad. For xampl, a im 1, ( ) crosss h +1 hrshold. An vn i 1 x ( ) ( )

6 (, 1 1, 1) is gnrad signifying ha i crosss h hrshold +1 a im 1. This discr vn gnraion mchanism producs a discr vn flow ha dscribs h voluion of h procss sa rajcory ovr im (Figur 11). Th discr vn flow is a sui of discr vn dscribing h ims of h nry in a nw spac ara x & +2 i Figur 12: Pic Wis Consan Trajcory Th discr vn flow dfins a pic wis consan rajcory (Figur 12) ha consius a modl of h procss sa rajcory. Th wo rprsnaions ar quivaln: h pic wis consan rajcory is a coninuous modl whil h discr vn flow is a discr vn modl and boh ar coninuous im modls. This mchanism lads hn a a mporal sgmnaion. This is similar o a mporal sampling wih a sampling priod ha is no consan. 4.2 Trajcory Inrpraion In ordr o inrpr h discr vn flow, h discr vns can b posiiond in a phas plan. Figur 13 illusras h inrpraion of h vn flow of h Figur 11 in a phas spac of Poincaré. & Figur 13: Trajcory Inrpraion Th inrpraion principl of h discr vn flow consiss in considring ha a discr vn dfins a poin in h phas plan. Th coninuous procss sa rajcory can hn b rconsrucd whn inrpring h poins dfind by h vns as h angn poin of h procss sa rajcory: a h im of h discr vn, h procss sa rajcory coincids wih h poin dfind by h vn. Thn 6 5 linking h angn poins wih a curv producs an inrpraion of h procss sa rajcory (Figur 13). This approach has similariis wih h Gnralizd-Dvs approach (Giambiasi & al, 2). Th xprs dfin a paricular rajcory corrsponding o an voluion wih a maning of paricular inrs. Th inrpraion principl allows h rprsnaion of h xpr s knowldg undr h form of a succssion of discr vn yp ha consius a signaur. Such a signaur is rprsnd wih a chronicl for h signaur rcogniion. 1 : E9 2 : E11 3 : E13 Figur 14: Exampl of Problmaic Squnc. For xampl, h squnc 8 o 13 shows a significan dcras of (Figur 13). Th problmaic aspc of such a rajcory is h succssion of an vn of h sam yp as 9, labld E9, followd by an vn of h yp of 11 (E11), and h lar followd by an vn of E13 yp (Figur 14). This siuaion, wih h appropria mporal consrains, mans ha a vry low-lvl (E13) has bn rachd wih an almos null drivaiv (E11) afr a srong dcras (E9). If h goal is o hav a normal lvl, somhing mus b don. Th rcogniion of his significan sgmn in a discr vn flow uss h signaurs dfind by h xprs. Whnvr a squnc of vns saisfis h logical and imd consrains of h chronicl ha rprsns a signaur, a nw discr vn is gnrad. 4.3 Rcursiviy Propry Th quanizaion procss gnras h firs lvl of discr vn. From his firs lvl, h signaur rcogniion procss gnras a scond lvl of discr vn. Th vns of his scond lvl ar mor absrac han hos of h firs lvl. On h advanags of h discr vn paradigm is ha his rprsnaion allows h dsign of a rcursiv procss: h discr vn of h scond lvl can also b usd in ordr o rcogniz signaur of anohr yp. This scond sag of rcogniion will gnra a hird lvl of discr vns ha can again b usd for a hird sag of signaur rcogniion, and so on. Th prcpion-basd diagnosis approach lads hn o a rcursiv and holographic dsign. Th procss bhavior is dscribd a diffrn absracion lvl wih a uniqu paradigm, h discr vn paradigm, and h ransiion from a lvl of absracion o anohr is basd on a similar signaur rcogniion procss. As a consqunc, a ach lvl of absracion, h rasoning procss is similar. For xampl, h knowldg of Sachm dfins 3 lvls of discr vns (Figur 15): Th Signal Evn lvl. A his lvl, an vn is conncd wih h maching of a bhavior modl and a signal. Th Signal Phnomnon lvl. This lvl of vn dscribs a paricular voluion of

7 h signal, according o a norm (i.. a signaur). This is a coupl of Signal Evn, dcd on h sam variabl, on ha ims h sar of h Signal Phnomnon and on for h nd. Th Procss Phnomnon lvl. This lvl aggrgas diffrn signal vns gnrad from diffrn variabls in ordr o rcogniz h spcifics ffcs of a phnomnon. A Procss Phnomnon is also a coupl of vns, on for h bginning of h phnomnon and on for h nd, h im coms from h signal vns uss o rcogniz h procss phnomnon. Procss Phnomnon Evns Signal Phnomnon Evns 1+ Los vn. This vn indicas ha h maching algorihm is unabl o dcid if a modl machs or no. This cas occurs whn daa ar no prsn or ar invalid. Signal S Modls for signal S Modl Maching Figur 16: Modl Maching Evns of Signal S Ths hr yps of vns ar calld signal vns and consiu h basis for h prcpion analysis. Th informaion concrnd wih h gnraion of h vn (snsor, spac ara, signal procssing algorihm, naur of h vn, c) is associad wih h vn in a signal vn objc (cf. h chnical dsign scion). TL xm 1 =a 1 +b 1, a 1 >S 1 xm 2 =b 2, b 2 >S 2 1+ Signal Evns + r 2 1 Ts 1 T 1 Ts 2 T 2 1 Signal Figur 15: Th Thr Lvls of Discr Evns Th rcursion propry is inrsing for many diffrn aspcs in paricular for h rprsnaion of mporal knowldg and h dsign of monioring and diagnosis cogniiv agns. Th sam paradigm can b usd o rprsn h knowldg rquird for diffrn sags of rcogniion procss. Th nx scion illusras his approach wih h xampl of h dcion of a scaffolding phnomnon by Sachm. 5 PERCEPTION OF A PHENOMENON Th firs lvl of h discr vn gnraion of Sachm is applid o signals and is achivd wih Signal Procssing algorihms and arificial nural nworks (L Goc al., 1998). 5.1 Noion of Signal Evn Th principl is basd on h maching of a sgmn of a signal wih a modl (Figur 16). Thr yps of vns ar rquird: Sar vn. This vn indicas ha h bhavior of a signal S machs a paricular modl. Such an vn is gnrad whn h qualiy cofficin of h maching is grar han h rquird hrshold. End vn. This vn indicas ha h currn modl no longr machs. This vn is h opposi of h maching vn Figur 17: Sp #1 Modl Maching 5.2 Signal Evn Prcpion In Figur 17, h vns ar producd wih a ral im linar rgrssion algorihm applid o h Thrmal Load (TL) variabl. This variabl masurs h nrgy ha flows ou h blas furnac via sack. Th r 2 variabl masurs h qualiy of h linar rgrssion. Th maching is achivd whn his qualiy cofficin is ovr a minimum hrshold, ypically.8, and whn h cofficins ( a i and b i ) of h modl rspc h valus dfind by h xprs. Th firs sp of h discr vn gnraion is h producion of h Sar and End vns whn a signal machs wih a modl (Figur 17). TL r 2 1 s 1 1 s Figur 18: Sp #2 Signal Evn Gnraion Th nx sp drmins h acual im of h voluion. Bcaus h analysis is achivd in ral im, h vn dcion inroducs a dlay in h 5

8 signal analysis. Whn an vn is dcd, h signal is analyzd from h prsn o h pas, in ordr o radjus h acual modl paramrs (Figur 18). Th signal vns ar hn gnrad wih 2 ims, h discr vn dcion im and h sar im (s i ) or h nd im ( j ) of h signal bhavior. Bcaus a discr vn is codd as an objc wihin Sachm, vry discr vn insancs conains all informaion concrning h parn maching (signal idniy, h machd modl wih h currn paramrs, c). 5.3 Signal Phnomnon Prcpion Th flow of signal vns is hn analyzd according o h s of signaurs for h TL signal. For xampl, h rcpion of h squnc {1, 2, 3, 4} may saisfy a Thrmal Load Sabiliy signaur (Figur 19). Sachm will vrify h imd consrains ( T i ST i ) and h valus of h paramrs (α 1 and β 2 ). TL xm 1 =α 1 +β 1 [s 1, 1 ] T 1 S 1 T 2 S 2 T 3 S 3 xm 2 =β 2 [s 2, 2 ] s 1 1 s Figur 19: Sp #3 Evn Flow Analysis If hs consrains saisfy h rquirmns, a Thrmal Load Sabiliy signal phnomnon will b gnrad. Th bginning of his signal phnomnon will b imd a s 2 (Figur 2). Th vn daa ar usd o analyz h voluion of a signal phnomnon (qualificaion of h magniud, localizaion according o h gomrical posiion of h snsors, c). In h sam way, Sachm drmins h nd of a signal phnomnon wih ddicad chronicls. 5.4 Procss Phnomnon Prcpion Th flow of signal phnomnon is hn analyzd in ordr o idnify h Procss Phnomnon ha occurs insid h blas furnac (Figur 21). In h xampl, h signaur of h Scaffolding procss phnomnon will b usd o dc a nw insanc of his phnomnon ha sars a im s 2. Such a signaur will mrg 3 signal phnomna in ordr o dcid h gnraion of h insanc of h Scaffolding procss phnomnon. TL Signal Phnomnon Thrmal Load Sabiliy sard a s 2 Figur 2: Sp #4 Trajcory Inrpraion A procss phnomnon has a spcific signal phnomnon for h dcion of is bginning and anohr on for h dcion of is nd. Bwn s 2 hs wo ims, h signal phnomnon flow is analyzd in ordr o qualify h voluion of h phnomnon. For som procss phnomnon, h 3 main opraions (h dcion of h bginning, h qualificaion of h voluion and h dcion of h nd) mus mrg h signal phnomnon in h spaial dimnsion. TL Signal phnomnon Low Thrmal Load Signal phnomnon Thrmal Load Dcrasing s 2 Signal phnomnon Thrmal Load Sabiliy BF phnomnon Scaffolding saring a s 2 Figur 21: Sp #5 Procss Phnomnon Dcion For xampl, h wall mpraur of a blas furnac sack is masurd hrough a marix of 8*13 virual snsors (a virual wall mpraur snsor is an aggrgaion of a las 3 physical snsors). Th dcion of h bginning of a High Lvl Tmpraur on h Wall rquirs h aggrgaion of a las 4 nighboring snsors. Th phnomnon is hn locad wihin h marix and h spaial voluion is analyzd along im: a phnomnon can incras and mrg wih anohr insanc, or disappar. 6 SACHEM OVERVIEW Sachm dscribs h currn physical phnomna, ha work wihin h blas furnac and qualifis hm in rms of alarms (or warnings) according o h currn opraional procss conx. Th aim of h alarms is o warn human-opraors abou h unsaisfacory bhaviors of h blas furnac in ordr o avoid anomalis or incidns in h shor and midrm. 6.1 Th Global Rasoning Procss of Sachm Th Sachm prcpion funcion dscribs hus h sa of a blas furnac as a flow of phnomna ha sar, nd a paricular ims and volv ovr im and spac as h procss islf volvs. Sachm organizs h opraing phass of a blas furnac in conx ha allows o qualify ach insanc of phnomnon in rms of alarm, warning or no problmaic (his is a valu of h svriy slo of a phnomnon insanc). Whn a phnomnon is an alarm or a warning, a mssag is sn o h human-opraors (Figur 22). A conx considrs h absnc of paricular insancs of phnomnon ovr mporal priods. Such priods characriz h mor or lss good qualiy of h procss conrol (Albrs & Ghallab, 1997). Th graphical usr inrfac of Sachm is dividd ino 3 pars: h insrumnaion saus (lf), h mssags ha dscrib h currn phnomna (uppr righ) and h mssags rcommnding corrciv acions (lowr righ). By clicking on h mssags, h usr accds o h viws ha jusify Sachm s

9 dcisions hrough a s of curvs rprsning h voluion of h main variabls ha ar concrnd wih h maning of h mssag. Daa acquisiion, synchronizaion, and vrificaion of h 1,1 daa pr minu. Daa ordring, procssing, validaing (using physical and chmical modls) and diagnosing o idnify h damagd snsors. This funcion procsss up o 4,5 daa ach minu in ordr o gnra h signals o b analyzd. 1,1 daa/mn Monioring ~7 Msg / day Alarms/Acion Daa Acquisiion & Modl Procssing Sa Prcpion & Diagnosis Sa Corrcion Opraor Communicaion Jusificaions Insrumnaion compur Daa Bas Managmn ~5,5 Variabls Procss DB Evn DB Figur 24: Sachm Organic Archicur Figur 22: Sachm Usr Inrfac Signal analysis and parn rcogniion, including nural nworks, o dc signal modificaions along h dimnsions of im and spac. This funcion prforms a mporal sgmnaion of around 5 signals o produc h firs flow of discr vns ( Signal Evn ). Th mssags ar displayd unil h undrlying problm disappard (i.. h phnomnon nds or h acion is no mor rquird). Th whol discr vn basd absracion procss dscribd in his papr aim a managing h mssags ha ar concrnd wih h phnomnon working insid h blas furnac. Logical Snsors Sa Prcpion Signals Quanizaion Signal Evns Discr rajcoris Inrpraion Signal Phnomna Dcion Procss Sa Procss Phnomna Procss Sa Diagnos Corrc Logical Acuaors Procss Phnomna Rcogniion Procss Bhavior Procss Bhavior Analysis Procss Problms & Causs Rcommnd Rcommndd acions & warnings Figur 23: Th Global Rasoning Procss of Sachm Th global rasoning procss of Sachm is dscribd in Figur 23. Th prcpion funcion aims a dscribing h sa of h blas furnac ovr im. Th prcpion funcion of Sachm supplis a diagnosis funcion o compl h dscripion of h sa of h conrolld procss (Figur 23). This funcion analyzs h flow of phnomna in ordr o rcogniz h problmaic bhaviors, o idnify h hypohical causs ha xplain h rcognizd bhavior, and o advis which unsaisfacory sas h procss could ponially rach in h fuur. Th diagnosis funcion is currnly bing dsignd wih h sam rcursiv approach. 6.2 Sachm Archicur Figur 24 shows h archicural principl of h Sachm sysm ha suppors h implmnaion of h global rasoning procss of Sachm. This archicur is s up o procss mor han 1,1 daa ims pr minu for a givn blas furnac. According o his modl, Sachm xcus h following main asks: Phnomna dcion. Th flow of discr vns crad from h signals is analyzd ach minu in ordr o rcogniz spcific bhaviors ha indica h prsnc of phnomna. Each phnomnon is localizd in h blas furnac and is spaial and mporal magniud is qualifid according o a spcific s of rangs (ypically; vry low, low, normal, high, vry high). According o h s of currn phnomna ha dfins h conx, ach phnomnon can b qualifid as warnings or alarms in ordr o alr h human-opraors. Acion Rcommndaion for h humanopraors, whn a siuaion mus b corrcd. Th rquirmns for corrcion ar valuad for ach phnomnon and an nrgy balanc is compud. Th siuaion in is niry is hn valuad in ordr o drmin h adjusmn of h acuaors (magniud and im). A usr-frindly and conx-dpndn man machin inrfac ransmis h warnings and h alarms o h human-opraors and prsns all h rlvan informaion ha is ncssary o undrsand h imporanc of h phnomnon in h currn siuaion. Th dvlopmn of Sachm ( ) rprsns an invsmn of 3 million uros (~15 man.yar). Th am was composd of 2 o 3 nginrs (including 6 knowldg nginrs) and 12 xprs. Ths considraions lad o mhodological problms. 6.3 Sachm Mhodology According o A. Nwll dfiniion, Sachm is a raciv raional agn, calld a Monioring Cogniiv Agn ha collaboras wih humanopraors in h conrol room (Figur 25) (L Goc Gaéa, 23).

10 Th CommonKADS mhodology provids a gnral framwork for h dvlopmn of cogniiv agn as knowldg basd sysms (Schribr al, 2), [Brukr & Van d Vld, 1994). Blas Furnac Insrumnaion (Acuaors) Insrumnaion (Snsors) Suprvision sysm Acions Goal: To opimiz h BF bhavior Procss Daa SACHEM Opraor Opraor Evns Figur 25: Sachm is a Monioring Cogniiv Agn This framwork proposs guidlins o build an informal modl, known as h Concpual Modl of h sysm, which mus b compld o bcom a spcificaion modl. So h concpual spcificaion phas ha producs h concpual modl compl h classical funcional spcificaion phas (producing h spcificaion modl) and h chnical dsign (producing h dsign modl) (Figur 26). Th knowldg-basd sysm dvlopmn can hn b viwd as h addiion of a knowldg acquisiion procss o h classical sofwar dvlopmn procss (L Goc al., 22). Th CommonKADS concpual modl is buil in an implmnaion indpndn way. This modl is basd on h idnificaion of diffrn yps of knowldg, which ar disinguishd as hr diffrn layrs wihin h KADS concpual modl: Th domain layr is h lows layr. I conains knowldg abou h applicaion domain of h sysm, ha is, h concps and hir rlaionships, usd as rols in h nx layr, and h rasoning ruls. This knowldg is dscribd frly from bhavioral considraions; ha is, from h way his knowldg will b usd. Bhavioral informaion is inroducd in h ohr layrs. Modls A b s r a c i o n Ral World Concpual Modl Knowldg Modling Linguisic Modl Knowldg Acquisiion & Analysis Businss Knowldg Rquirmns KBS Dvlopmn Sofwar Knowldg Funcional Spcificaion Dsign Modl Tchnical Dsign Dsign Modl Coding Ingraing Knowldg Basd Sysm Figur 26: Modl Transformaion of SachmKADS mhodology Th infrnc layr spcifis h basic infrnc sps wih infrnc srucurs. An infrnc srucur is composd of infrncs and rols and dscribs h ransformaions ha h us of h domain knowldg can opra. Th ask layr is composd of a hirarchically organizd s of asks. Each ask rprsns a problm-solving procdur. I is composd of infrncs and/or subasks and i spcifis h conrol of hir acivaion (squnc, iraion, condiional samn, c). Th concpual modl of h knowldg spcifis h nir sysm. This modl conains 25, objcs for 33 goals, 27 asks, 75 infrnc srucurs, 32 concps and 2 rlaions. This rprsns 14 manyars of work for a am of 6 knowldg nginrs and 12 xprs during a priod of 3 yars. Th concpual modl is wo pars: a gnric modl of inrpraion, and a linguisic modl, which is a rprsnaion in naural languag of h domain knowldg. This linguisic modl is mad of 2 pags of x and graphs, which ar disribud in ovr 7 documns (Figur 26). Th xpliciaion of h linguisic modl is on of h modificaions ha hav bn don in ordr o adap h CommonKads mhodology for h Sachm dvlopmn. 6.4 Tchnical Dsign Th Sachm concpual modl has bn implmnd in 21 knowldg bass in ordr o rcogniz (i.. o dc, o localiz and o confirm) around 175 classs of phnomna. All hs ims of knowldg ar implmnd wih KOOL-94, a powrful languag ha combins concps of: Objcs (classs and ma-classs, mhods and damons), Firs ordr logic ruls. Th dduciv rasoning procss is drivn by h daa wih forward and backward chaining algorihms. Chronicl. A mporal managr basd on h Alln logic analysis h flow of vns o consruc h squncs ha saisfis h chronicls. Th chronicls ar usd o implmn h xpr s signaurs. Procdurs. A C-lik languag prmis h managmn of h insancs ha consiu h mmory of h sysm and h communicaion wih ohr sofwar and h nvironmn. Th Sachm knowldg bass conain mor han 16 classs of objcs, 11 firs ordr logic ruls and 14 chronicls. Daa Acquisiion & Modl Procssing 2% Sa Prcpion & Diagnosis 33% Monioring 7% Sa Corrcion 1% Daa Bas Managmn 1% Figur 27: Knowldg disribuion Opraor Communicaion 2% Th oal sofwar volum rprsns around 4 lins of cod. Abou 6% of his amoun of cod implmns h Sachm funcional modl, 33% of which is ddicad o h knowldg bass (Figur 27). 6.5 Sachm Economic Rsuls Figur 28 shows h ffc of using Sachm on h frquncy of incidns.

11 Th frquncis hav bn masurd during 1996 on h wo blas furnacs of Fos-sur-Mr, which ar idnical, bu only on was quippd wih Sachm. Th original lvl is h rfrnc lvl obaind during Rfrnc Lvl Expcd lvl Wihou Sachm Frquncy of Incidns Wih Sachm TOTAL Burdn Dscn Thrmal Losss Low Ho Mal T On of h advanags of h discr vn paradigm is h compacnss. For xampl, on yar of blas furnac rprsns around 3, procss phnomna in a s of x fil of 4.7 mb. This discr vn flow is h discr vn absracion of a daabas of 235, mb conaining h blas furnac raw daa. Th compacnss facor is hn 5,. Targ Rsul Sachm dos no implmn h prcpion ask of Figur 28: Sachm dcras h Frquncy of Incidn Th incidns ar classifid in 3 yps (Low Ho Mal Tmpraur, Thrmal Loss and Burdn Dscn). Figur 28 shows ha Sachm dcrasd h global numbr of incidns undr of h xpcd lvl and oally avoidd h mos srious yp of incidns: a low lvl of h ho mal mpraur (which can lad o a dsrucion of h blas furnac). Sachm arns currnly bwn 1,5 and 1.7 pr on of pig iron. Wih six blas furnacs quippd wih a Sachm sysm, Arclor arns bwn 16,5 and 18,5 millions uros pr yar. Th chnical and conomical succss of Sachm has ld o h dsign of Sachm-lik sysms o assis h conrol of ohr indusrial producion procsss. And oday, Sachm is a commrcial sofwar produc availabl on h world mark. 7 CONCLUSION Sachm dscribs h currn physical phnomna, and qualifis hm in rms of alarms or warnings according o h currn opraional procss conx. Th aim of alarms is o warn human-opraors abou h unsaisfacory bhavior of h blas furnac in ordr o avoid anomalis or incidns in h shor and mid-rm. Th basis of h Sachm rasoning is h rcogniion of signaurs ha ar rprsnd wih chronicls, a s of imd consrains linking a s of discr vn yps. Th discr vn paradigm allows h dsign of a rcursiv discr vn absracion procss basd on h rcogniion of signaurs. W calld his anhropomorphic approach of h procss conrol h prcpion-basd diagnosis. Sachm analyzs coninuously a flow of discr vns of mor and mor absrac lvl. Th prcpion-basd diagnosis is rcursiv and holographic: h procss bhavior is dscribd a diffrn absracion lvl wih a uniqu paradigm, h discr vn paradigm, and h ransiion from a lvl of absracion o anohr is basd on a similar signaur rcogniion procss. As a consqunc, a ach lvl of absracion, h rasoning procss is similar. xprs, which is a oo complx and mysrious cogniiv procss o b dscribd. Howvr, wih h sam inpu daa flow, h chniqus implmnd in Sachm producs h sam oupu daa as h bs Arclor xpr in h Sachm skill ara. Bu h inroducion of Sachm in h conrol room had an inriguing ffc: h knowldg concrning h causal rlaions ndd o b rvisd by xprs. This rvision is h rsul of h inviolabl daing ruls ha ar sysmaically and rigorously applid by Sachm in ordr o im h phnomnon (sar and nd). Gnrally spaking, h mporal rlaions bwn phnomna mus b updad, and in som cass, vn h dircion of h rlaion mus b rvisd. To his aim, w ar now dvloping an approach o assis h xpr in h discovring procss of signaurs a h procss phnomna absracion lvl. Our approach is basd on h Markovian rprsnaion of a discr vn flow. Ingrad in a Java nvironmn calld h ELP Lab, a s of ools has bn dvlopd o compu corrlaion bwn h bginnings of procss phnomnon. Th ELP languag is a high-lvl knowldg rprsnaion languag of signaurs (Frydman & al., 21). Th ELP signaurs ar opraionnalizd wih h DEVS formalism: h chronicls ar ranslad ino DEVS modls ha a DEVS simulaor uss in ordr o rcogniz h discr vns squncs ha saisfy h signaurs. Our currn work aims a dfining a mhod o infr an ELP signaur from a s of significan squncs. ACKNOLEDGEMENTS Th Auhors would lik o hank N. Giambiasi for is hlpful commns on arlir drafs and valuabl discussions. REFERENCES Aguilar J., Bousson K., Dousson C., Ghallab M., Guash A., Miln R., Nicol C., Quvdo J. and Travé-Massuyès L. (1994). TIGER: Ral-Tim Assssmn of Dynamic Sysms. Inllign Sysms Enginring, pp Albrs P., Ghallab M. (1997). Conx dpndn ffcs in mporal planning. Rcn advancs on AI planning, Sl and Alami (Eds.), p.1-12, LNCS 1348, Springr, 1997 Brdill P., Dlouis I., Eyrolls P., Jhl O., Krivin J.-P. and Thiaul P. (1994). Th AUSTRAL Expr Sysm for Powr Rsoraion on Disribuion Sysms. In Procdings ISAP 94,

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