FAULT TEMPLATE EXTRACTION FROM INDUSTRIAL ALARM FLOODS. Sylvie Charbonnier, Nabil Bouchair, Philippe Gayet

Transcription

1 FAULT TEMPLATE EXTRACTION FROM INDUSTRIAL ALARM FLOODS Sylve Charbonner, Nabl Bouchar, Phlppe Gayet

2 Industral control systems based on SCADA archtecture Human-Machne Interface SCADA servers PLC Varables Dagnostc Process Alarms gpsa-lab

3 Dagnostc may be complex Human- Machne Interface Scada server gpsa-lab Fault Alarm flood More than 0 alarms durng 0 mn (ANSI/ISA-8,2)

4 To assst the operator confronted to an alarm flood Fault template: learnt from a set of alarm lsts recorded durng the occurrence of the fault Learn nformaton on the alarm generaton process (off lne) Whch alarm s relevant? Whch alarms are specfc to a gven fault? Is a fault dagnosable from the alarm lst? Is there a specfc pattern n the alarm flood? sequental : The alarm order of appearence s used vectoral Alarms are weghted accordng to ther relevance to the fault Decson support n operaton (on lne) Suggest possble faults gpsa-lab

5 Outlne. Methods A. Template extracton sequental template vectoral template weghted sequental template B. Knowledge extracton and decson support 2. CERN LHC process 3. Results gpsa-lab

6 Learnng set f faults k : : : : : :. N = full number of alarms trggered by the control system gpsa-lab

7 Sequental fault template N = full number of alarms trggered by the control system of sze N An alarm lst = a symbolc sequence f faults k : : : : : :. Sequental fault template : the mnmal sequence formed of alarms present n the gpsa-lab same order on more than half the lsts generated by fault n q

8 Template extracton usng the Needleman and Wunsch algorthm The Needleman and Wunsch algorthm (970) Used n bo-nformatc to compare sequences of genes Use dynamc programmng Global optmal algnment of 2 symbolc sequences Insert gaps to ncrease smlarty A gpsa-lab

9 Smlarty between 2 sequences Tunng parameters: cost of a gap =0 C ( ) costof susbttutngone elementwth another + - Smlarty ndex: sm NWA gpsa-lab ( S a, S a 2 ) l σ( s p = = a p l, s a 2 p ) σ( s a p, s C = l a 2 p )

10 Template extracton : multple algnment k sequences to algn ascendent herarchcal clusterng Template : Appear n more than half the lsts <T,G,C,C,G,T> gpsa-lab

11 Outlne. Method A. Template extracton sequental template vectoral template weghted sequental template B. Knowledge extracton and decson support 2. CERN LHC process 3. Results gpsa-lab

12 Vectoral alarm lst representaton f faults Alarm Alarm 2 k : : : : N [ ],q,q,q v v... v,q V = 2 wth [,..., f V,q N T ] and q [,...,k ] s the q th alarm lst of fault : : Alarm N N. gpsa-lab

13 Weght w : relevance of alarm to fault compared to faults contaned n S w = (2* α )*( β ) α k δ ( v q = =, q k p Probablty that alarm s equal to p n the k examples of fault ) p : value of alarm for the maorty of the k examples of fault p p = = f 0 f f x = 0 δ (x) = 0 f x 0 k q = k q = ν k ν k, q, q < β h S = k δ ( v q= m h, q Probablty that alarm s equal to p n the examples of faults contaned n S k p ) Wth S : a set of faults excludng fault ; m=card(s ) w depends on the faults contaned n S gpsa-lab

14 Example : alarm F F F F F F2 F2 F2 F2 F2 F3 F3 F3 F3 F F compared wth F2 and F3 : p = α = 3 5 F compared wth F2 : p = β = ω = (2 * ) * ( ) = 0.2 * 0.5 = α 3 = 5 β = 3 ω = (2 * ) * ( ) = 0 5 F2 compared wth F3 : 2 = 2 = 2 = 2 = p α β 0 ω = (2 *) * ( 0) gpsa-lab

15 Outlne. Method A. Template extracton sequental template vectoral template weghted sequental template B. Knowledge extracton and decson support 2. CERN LHC process 3. Results gpsa-lab

16 Weghted sequental template : T w = ( 2* α )*( β ) ( 2* α ) = gpsa-lab

17 Outlne. Method A. Template extracton sequental template vectoral template weghted sequental template B. Knowledge extracton and decson support 2. CERN LHC process 3. Results gpsa-lab

18 Wegth matrx representaton w w = w Alarm number W 2 N Fault number gpsa-lab

19 Wegth matrx representaton W wth S = {,,f}\ max( w ) for = to f max( ) for = to w N gpsa-lab

20 Knowledge extracton : fault dagnosablty Defntons : - An alarm s specfc to a fault compared to a subset of faults S f = w - f a fault has at least one specfc alarm, t can be dstngushed from the faults contaned n S, from ts vectoral representaton gpsa-lab

21 Dscrmnablty between pars of faults (,k) w w = w W 2 N Wth S = {k} W = Alarm s specfc to fault compared to fault k Alarm can dstngush fault from fault k For k= to f except, End F = { } gpsa-lab W S = {k} Calculate usng If max = ( : N w ) = fault can be dstngushed from fault k else t can be confused wth fault k : k F Fault s dagnosable from ts alarm lst

22 gpsa-lab Decson support n operaton : smlarty between unknown alarm sequence S and template T Weghted smlarty between S and template T (length : q) + α + α + α = N w w w C + σ = = = q h h h l p a p a p q l t t C s t T S WS ) ( ), ( ), ( ), ( = C =0 l s t T S sm l p a p a p NWA σ = = ), ( ), ( =0 Unweghted smlarty between S and template T (length q) l ), ( a p t a p s σ Alarms present n T number of gaps added

23 Decson support n operaton : nformaton dsplay smlarty between S and fault templates gpsa-lab

24 Outlne. Method A. Template extracton sequental template vectoral template weghted sequental template B. Knowledge extracton and decson support 2. CERN LHC process 3. Results gpsa-lab

25 Large Hadron Collder n CERN large rng of 27 km 00 meters underneath Collson of 2 beams of protons accelerated at the lght speed Dscovery of new partcules gpsa-lab

26 LHC : Large Hadron Collder systems the bggest partcle accelerator n the world formed of many dfferent systems all controlledusngthe samescada archtecture The gas system : supply the detecton chambers wth a precse mxture of pure gases gpsa-lab

27 CERN LHC process : the gaz system MIXER BUFFER EXHAUST PURIFIER AIR MIX PUMP CHAMBRES gpsa-lab

28 Accurate smulator of the gas system IHM Supervson layer Lnked to the real control system Faults can be created Server Scada PLC Model Control layer Algebro-dfferental equatons 995 equatons Valdated from comparson wth the real system C++ 2 gpsa-lab 8 Feld layer

29 f= 3 faults generated 5 MIXER BUFFER 2 EXHAUST k=6 alarm lsts per fault N=60 alarms (trggered at least once) PURIFIER AIR MIX Leaks blockage of controllers stoppage of pumps broken bubblers (safety devce) sensor problems leadng to regulaton ssues 3 PUMP CHAMBERS 2 65/78 Alarm Floods (>0 alarms n 0 mn) gpsa-lab

30 Outlne. Method A. Template extracton sequental template vectoral template weghted sequental template B. Knowledge extracton and decson support 2. CERN LHC process 3. Results gpsa-lab

31 Alarm lsts length Template length Number of alarmrs gpsa-lab Fault number Percentage of alarm floods

32 gpsa-lab Smlarty between sequental fault template : unweghted smlarty Alarm lst averaged length l = C

33 Wegth analyss w wth S = 3 faults except max( ) for = to3 w 7% of alarms are rrelevant gpsa-lab max( w ) for = to60 Faults 4 and 9 can be dagnosed

34 Weghted sequental fault template Fault 4 Fault 9 gpsa-lab

35 Fault dagnosablty (vectoral template) If max = : N ( w ) = where S = { } k fault can be dstngushed from fault k undstngushable dstngushable Faults 3, 4, 9, 0, 2, 3 are dagnosable from ther alarm vectors Confuson : {,2}, {5,6,7}, {8,} gpsa-lab

36 Fault / Fault 2 { } w wth S = k gpsa-lab

37 Fault 8/ Fault gpsa-lab

38 Fault 5/ Fault 6 Leak n Mxer/ leak n Exhaust Lack of nstrumentaton gpsa-lab

39 gpsa-lab Smlarty between sequental fault templates : weghted smlarty { } l S w = wth + α + α + α = N w w w C + σ = = = q h h h l p a p a p q l s s C s s S S WS ) ( ), ( ), ( ), ( α=0

40 Fault templates for = to 3 w wth S = 3 faults except w (2* α = (2* α ) = )*( β ) gpsa-lab

41 Decson support n operaton dagnose unknown sequence S For = to 3 WS( S, T ) = q C( t h= l σ( t p= h, t h a p, s a p ) ) + ( l q) w wth S = 3 faults except The most smlar fault: 0 Fault wth a smlarty hgher than 0.3 :, 0, 2 Weghted smlarty between S, and T gpsa-lab

42 Fault 0 Fault 2 Fault gpsa-lab

43 Classfcaton accuracy usng the frst nearest neghboor For eachof the 78 sequences Classfcaton : Assgn unknown sequence S to the fault whose template s the most smlar(weghted smlarty) Valdaton : leave one out Results: -removes fromthe data set - extract fault templates T wthout S Vectoral representaton; Hammng dstance : 54/78 Sequental representaton; weghted smlarty: Full template, full alarm sequence: 75/78 gpsa-lab

44 Impact of the template length TmaxTmax Tmax Tmax gpsa-lab

45 Impact of the template length on the classfcaton accuracy The templates can be shorten to 35 alarms. Tmax gpsa-lab

46 Impact of sequence length Whenmakethe decson? Is tnecessaryto watfor the end of the alarm flood? S sreducedto tsl frst alarmsthe decsonsmade afterl alarms at the latest L=30 gpsa-lab

47 Impact of sequence length Number of alarmrs gpsa-lab Fault number

48 Concluson Off lne : A strategy to learn nformaton from a fault data set valuable feed-back nformaton on the alarm generaton process gpsa-lab Irrelevant alarms Fault dagnosablty from ts alarm lst Lack of nstrumentaton Typcal fault pattern : gudelne for non expert operators On lne : A strategyto dagnose a faulton the occurrence of an alarm flood, based on smlarty No model requredbut a needfor data Boththe presenceand the absence of alarmsare consdered