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An Expert System for Diagnosis and Maintenance in FMS
An Expert System for Diagnosis and Maintenance in FMS V. D. Majstorovic, V. R. Milacic (1); Mechanical Engineering Faculty, Beograd University/Yugoslavia Received on January 25,1989 ABSTRACT; T h e building of the expert system for diagnosis and maintenance in FMS i s a very complex interdisciplinary engineering task. In I n t e r C e A T (International Center for Advanced Technology) a model o f a n expert system called EXMAS (Expert Maintenance System) h a s been developed for conceptual diagnosis and maintenance of FElS working stations. T h e e l e m e n t s of the theory of a u t o m a t a , k n o w l e d g e blocks, artificial intelligence t o o l s and techniques have been used in the building o f this model. I n i t s present s t a g e the model i s developed a s the prototype f o r certain working s t a t l o n s , s o that later suiiie o f i t s modules may be linkcd with i t s higher l e v e l s (betvrrn indivldual working s L a L i o n s ) and the integrated software product for other FMS functions.
KEY WORDS: Diagnosis, Expert Systems. F>IS, Maintenance.
1. THE APPROACHES TO THE DEVELOPMENT OF DIAGNOSTIC SYSTEHS - RESEARCH PROBLEM DEFINITION FMS to-day represent t h e highest level of f l e x i b l e automation widespread in industrial environment. These a r e computerised plants with high level data processing distribution and the automatic f l o w o f material, consisting from different mechatronic working stations. T h e development o f t h e concept o f “un-manned” machining s t a t i o n s gave the basic i m p u l s e for the development of diagnostic s y s t e m s a t a l l technoloeic a l levels in flexible manufacture. Kobayashi [ I ] attaches t h e greatest importance for t h e development of this concept t o t h e following three factors: the f l e x i b i l i t y , reliability and diagnostic system. T h e theoretical f u n d a m e n t s of diagnostic s y s t e m s for manufacturing s y s t e m s have been s e t down by prof. Yoshikava [2]. T h e more recent characteristic approaches include: t h e use o f fuzzy s e t s [3], c a u s a l model o f working s t a t i o n s [4] and the u s e of A1 in the s e l e c t i o n of diagnostic s i g n a l s [5,61. The diagnostic system o f working s t a t i o n s i s developed o n theoretical and experimental a n a l y s i s of i t s processes and a p p e a r a n c e s , Fig.1. W i t h s u c h working s t a t i o n diagnostic model the d i a g n o s i s i s made on t h e basis o f comparative a n a l y s e s o f real behaviour and simul.ated diagnostic parameters. T h i s paper has two parts. T h e fj.rsr: part describes developed EXNAS model i l l d e t a i l . The second p o r t presents some r e s u l t s o f practical tests.
2. EXNAS M O D E L During FElS working s t a t i o n lifecycle three s t a g e s may be idenlified: d e s i g n i n g , production and exploitation. T h e first t w o , a s a r u l e , relate to t h e producers and t h e third o n e to users.For this concept a multilevel mainLenance m o d e l has been developed which d e f i n e s the engineering a p p r o a c h e s to: (i)maintenance d e s i g n , (ii)maintenance technology d e s i g n , a n d (iii)mainLenance planning and c o n t r o l [ O ] . T h e model i s based o n a mechanical s y s t e m , and higher l e v e l s relate t o the a b o v e stated g r o u p s o f engineering activites (71. The entire model, the same a s each stratified level, has i t s o w n inputs. o u t p u t s and c o n n e c t i o n s with t h e superior-subordinated l e v e l s [ l o ] . ‘These information f l o w s were t h e basis f o r t h e building of EXNAS model whose principal architecture i s s h o w n in F i g . 2 . It h a s t h e following entities: (i)ES s h e l l (the k n o w l e d g e base a n d inference engine). (ii)the processor and (iii.)the communication i.nterface. T h e physical organisation of the working s t a t i o n i s a starting element: for the description of machine structure and thc determining of technical e f f e c t i veness parameters. T h e c o m p o n e n t s , mutually i n t e r connected with their mechanical, e l e c t r i c a l , thermal or other i n f l u e n c e s may prevent the failure of other components, which i s then defined with the k n o w l e d g e engineering p r o c e d u r e s o n t h e basis o f c o n n e c t i o n s and relations. T h i s may e n a b l e r e l i a b l e establishing o f d i a g n o s i s both for t h e condition-based maintenance technology and c o r r e c t i v e maintenance. T h i s approach is known a s c a u s a l model o r t h e first-principle based reasoning [ll]. T h e second a p p r o a c h , the heuristic reasoning i s s u i t a b l e for t h e representation of heuristic k n o w l e d g e , the description of designere n g i n e c r ’ s and maintenance p l a n n e r ‘ s cognitive processes. T h i s is particularly important in the appearo n c c o f n e w failures or s y m p t o m s , rrh1r.h s e r v e with the existing heuristic k n o w l e d g e about t h e system for establishing o f partial o r f u l l diagnosis. T h i s approach is t h u s s u i t a b l e when i t i s not possible to represent the c o m p l e t e k n o w l e d g e a b o u t m a c h i n e ‘ s
Annals of the ClRP Vol. 38/1/1989
s t r u c t u r e , i t s f u n c t i o n s and features. O u r research s h o w s that thc combined u s e o f both a p p r o a c h e s i s very convenient. f o r FMS working s t a t i o n s , which h a s been d o n e in the d e v e l o p m e n t of EXMAS. For t h e inference e n g i n e m o d e l , a s t h e second vital EXMAS module, he following p r i n c i p l e was a p p l i e d : f o r the model o f c o n c e p t u a l d e f i n i t i o n o f t h e condition-based maintenance t e c h n o l o g y , f o r w a r d c h a i n i n g , and for corrective maintenance technology back c h a i n i n g w a s used. T h e processor i s t h e second EXMAS e n t i t y , which i s f i l t e r i n g inference e n g i n e c o n c l u s i o n s through: (i)recogniLion procedures for d i a g n o s i s a n d maintenance (cases and processes). (iilenables t h e realis a t i o n o f corresponding logic o f general and s p e c i f i c c h a r a c t e r f o r the subject a r e a , and (iii)optimises c o r r e s p o n d i n g s o l u t i o n s o n the basis of o p t i m i s a t i o n c r i t e r i a , a c c o r d i n g to f u n c t i o n s , s t a t e s , processes and g o a l s . T h e diagnosis l o g i c o f working s t a t i o n s is based o n the research and modelling o f d i f f e r e n t above stated appearances and p r o c e s s e s , w h i l e t h e maintenance technology logic i s based o n : (i)results of d i a g n o s t i c process, and (ii)applied m o d e l s of maintenance technology. T h e economy and optimisation model o t d i a g n o s i s and maintenance r e l a t e s t o he procedures o f t h e maintenance cost optimisation and spare parts s t o r e s , he maintenance planner. appearing in The f u n c t i o n a l knowledge structure in EXMAS may be expressed with the following equation:
F S ~ = (F R ,SB,OB,S, K ) (1) where:FB-functional knowledge blocks, the most important f o r t h i s E S model being those related to different recognition classes, SB-structural blocks representing the k n o w l e d g e and relations in t h e s y s t e m of h i e r a r c h y , OB-operation k n o w l e d g e block b y which conclusion strategy is e s t a b l i s h e d , S coupling f u n c t i o n by which k n o w l e d g e acquisition model i s a c h i e v e d , and K-composition f u n c t i o n by which t h e reasoning model in the k n o w l e d g e base o f this ES i s established. T h e e n t i t i e s of d e c l a r a t i v e and heuristic k n o w l e d g e for the maintenance planner a r e represented through the s t r u c t u r e o f f a c t s and rules in Fig.3. T h e established model of the functional k n o w l e d g e s t r u c t u r e i s t h e basi., for finding c o r r e s p o n d i n g l a n g u a g e with g r a m m a r , and l o r the c o n s t r u c t i o n of a u t o m a t a [ 1 2 , 1 3 ] , s o that the k n o w l e d g e transformation h a s the form:
FS-G-A
(2)
where:L(G)-the language by which the transformation of f u n c t i o n k n o w l e d g e structure (FS) into s u i t a b l e grammar ( G ) is a c h i e v e d , and A-automaton c o r r e s p o n d ing t o t h e selected language and t h e c o r r e s p o n d i n g grammar. I n t h i s expert system t h e engineering logic i s automata-modelled, but oriented t o e n g i n e e r i n g logic with s o m e empiric c o n t e n t s also. T h e f o l l o w i n g s i t u a t i o n s in t h e diagnosis and maintenance a r e modelled with automat0n:maintenance s t r u c t u r e ; r e l a tions: symptoms-diagnoses, re1ations:diagnostic c o n clusions-maintenance t e c h n o l o g y , e t c . T h i s h a s been achieved with nondeterminate a u t o m a t o n in the form o f a q u i n t u p l e (input.starting s t a t e , transfer f u n c t i o n , s t a t e s and tinal state). W i t h linking o f executivii rules t h e c h a i n s a r e defined f o r t h e k n o w l e d g e acquis i t i o n and inference line. F i g . 4 s h o w s summarised a n a l y s i s o f r e s u l t s obtained with the s o f t w a r e s u p p o r t for a u t o m a t a , maintenance computer s t r u c t u r e s and selected mini machines in the “pilot“ FMS model. T h e l a r g e number o f reasoning l i n e s from 1 2 to 1 7 2 i s
particularly stressed. T h e y e n a b l e the realisation of d i f f e r e n t inference e n g i n e conclusions.
3 . E X M A S - SOME RESULTS OF EXPERIMENTS T h e r e l a t i o n s between certain k n o w l e d g e entities for maintenance technology d e s i g n e r , i.e. a psrt of t h e maintenance p l a n n e r , relating to maintenance planning o f work s t a t i o n s a r e shown in Fig.5. F o n terminate s y m b o l M M represents d i f f e r e n t work station maintenance s t r u c t u r e s , a n d the f a c t s representing declarative k n o w l e d g e . T h e structure and connections for a given mechanical s y s t e m a r e idenLified with the basic rules. T e r m i n a t e s y m b o l a i s defined as:a = (elements, features. quantity), 'and t h e followiAg o p e r a t i o n s a r e carried out over t h i s knnw1Pdgr:a = ( d e c o m p o s i t i o n , c o u p l i n g ) . T h e decomposition operahicns relate to t h e establishlng of the e n t i r e maintenance s t r u c t u r e o f work station,and the coupling i s carried out a c c o r d i n g L O r e l a t i o n s h i p r e l a t i o n s in maintenance s t r u c t u r e in question. T h e formalisaLion of t h i s k n o w l e d g e is made according to the r u l e s with the f o l l o w i n g form: S T R U C T U R E COMPONENTS: : =ELENENT (e1ement)IS-PART-OF(e1ement);STRUCTUKE CONNECTIONS::= E L E I I I E N T ( e l e m e n t ) I S _ A T O E l ( a t o m ) . These three g r o u p s of rules i n P R O L O G notation describe a l l possible f o r m s of t h e s t r u c t u r e o f t h e work station,including: hierarchical s t r u c t u r a l e l e m e n t s , c o n n e c t i o n s and i t s last element. O n the basis of similar hypotheses the d e f i n i t i o n and the formalisation of the k n o w l e d g e for other nonterminate and terminate s y m b o l s o f Lhe graph i n question a r e made. T h e problem of sched u l i n g o f planned maintenance i n WRP production c o n t r o l c o n c e p t in FNS. Fig.3. i s c o m p l e x , f o r which the question o f priorities between planned and corr e c t i v e maintenance has to be s o l v e d . In (81 detailed model i s presented. Here characteristic examples a r e quoted for t h e group o f strategic r u l e s , T h e first example i s : R U L E 4 (Priotity 2 for f a i l u r e removal); IF failure a t work s t a t i o n prioty 2 THEN apply Rule 4 on planned maintenance o p e r a t i o n s with probability o f 0 . 8 . T h e maintenance technology s c h e d u l i n g f o r FMS work s t a t i o n s i s made a c c o r d i n g t o t h r e e criteria: maintenance operations, maintenance work o r d e r s and mechanical s y s t e m t o be maintained. T h e strategic r u l e s f o r scheduled maintenance o p e r a t i o n s a r e classified i n t o f i v e groups. Example: R U L E 2 , I F priority 1 (certainty factor - 0 . 8 ) T H E N maintenance operations planned f o r the most s u i t a b l e production terms. Scheduling o f maintenance repair o r d e r s i s carried out a c c o r d i n g t o criteria for 9 priorities. Example: RULE 3 ; IF priority 2 (certainty factor - 0 . 8 ) THEN carry out work orders with t h e highest downtime c o s t s per unit of time. T h e third group o f rules rel a t e s t o the definition o f priorities for mechanical systems. T h e r e a r e 5 in a l l : RULE 1 ; IF priority 0 (certainty factor -0.9) T H E F mechanical s y s t e m s very complex. T h e failure o n them c a u s e s very g r e a t damage Translated i n t o the e n t i r e logic o f t h i s expert SYStem, f o r n o n t e r m i n a t e node-scheduling ( S C H ) f o u r l i n e reasoning may be established: PIS+ a7 a2 aJ dl d2 , Y S + a 7 bl d7 d2 , MS+ cr c 2 d4 d7 dg and EIS + f, d4 d, d 2 . T h e first reasoning l i n e relates to scheduling according to m a i n t e n a n c e operations, a n d t h e third and the fourth according to t h e maintenance history and the c o n t r o l o f spare part s t o r e s (maintenance orders) In the f u r t h e r text t h e e x a m p l e s o f r u l e s for maintenance planner f o r t h e work s t a t i o n PIC HBG80 a r e given. First two basic rules for nonterminate s y m b o l -DP a r e given, Fig.3. M C a s machining station has c e r t a i n quality parameters relating to t h e geomet r i c a l , k i n e m a t i c and operation a c c u r a c y , s o that the g e n e r a l form o f t h e rule for quality diagnosis parameters may be written in the following form: IF (quality f u n c t i o n ) ( p a r a m e t e r s ) T H E N ( e x a m i n a t i o n procedure). T h e e x a m p l e is: RULE 1 8 (quality diagnosis);IF parallelity o f machined s u r f a c e s A and B in housing No.016845 i s under 0.025 mm THEN establish parallelity o f main s p i n d l e i n relation to x-axis according to DIN 8620. For identification o f f a i l u r e t h e general f o r m o f t h e rule is: IF ( f a i l u r e - o c c u r r e n c e ) T H E N (place.with probability pi)and t h e e x a m p l e is:RULE 28; IF a f t e r completion o f machining c y c l e tool holder not released from main s p i n d l e THEN evidence suggested with probability 0 . i 5 .that failure o n lead-out valve o f pneumatics of tool c l a m p i n g system. Given r u l e s for condition based maintenance technology h a v e a c o m m o n form: IF (machine element) THEN (maintenance technology)(parameters). Example: IF front main s p i n d l e bearing of MC HBG-80 THEN exchange bearing when: (i)vibration amplitude a t measuring s i t e 1 during two successive mesurements 0 . 0 8 mm and 1 during two (ii)temperature a t measuring site s u c c e s s i v e measurements 85'C. T h e r e l a t i o n s between FMS c h a r a c t e r i s t i c s a r e defined with s t r a t e g i c rules applied maintenance technology m o d e l s , and procedures for s p a r e part storage control. E x a m p l e o f rule for s e l e c t i o n and application o f diagnostic procedures: R U L E 5 ; IF (i)main s p i n d l e o n c o n d i t i o n based maintenance and (ii)there e x i s t r u l e s with mention
of VIBRATIONS, and (iii)Lhere exist rules with word T E M P E R A T U R E THEN suggested evident (0.75) that r u l e s relating t o VIBRA'PIONS should be executed before rules with word TEMPERATURE. T h e presented ES model w a s tested a t the a b o v e mentioned working station. T h e k n o w l e d g e base for the diagnostic s y s t e m for ZIC HBG-80 w a s formed in PROLOG notation by forming t h e following f a c t s and rules: f a i l u r e occurrence probability, symptoms, definition of connections: diagnosis(e)-symptom ( u ) ~element (u)-technology(ej o f c o r r e c t i v e maintenance and element (u)-spare part (parts). T h e chaining i n the k n o w l e d g e base i s achieved with t h e u s e of PROLOG f e a t u r e s , backward r e t r i e v a l , when i n interactive dialogue, ES answers, corresponding f a c t s a b o u t the symptom a r e entered for t h e c h a i n i n g of hypotheses. T h e c o n c l u s i o n s a r e d r a w n via c e r t a i n t y f a c t o r , calculated with B a y e s formula.Conflicting s i t u a t i o n s a r e solved with selectivity c o e f f i c i e n t s , representing the s u m o f Bays probability r e l a t i o n s o i the e v e n t : s y m p t o m - d i a g n o s i s . I n order to check t h e basic f e a t u r e s of developed ES, i t w a s tested o n 39 different e x a m p l e s f o r t h e above mentioned MC. T h e folowing r e s u l t s w e r e o b tained f r o m a n s w e r s received and e x p l a n a t i o n s g i v e n by reasoning lines: (i) absolutely a c c u r a t e a n s w e r s : 10 t e s t e x a m p l e s o r a b o u t 2 5 X ; (ii) e x a c t a n s w e r s 17 test (answer reliability interval:0.9-0.6), e x a m p l e s o r 4 5 % ; (iii)partly exact ansuers.requiring additional a n a l y s e s (reliability interval:0.4-0.6). 8 t e s t e x a m p l e s , o r a b o u t 2 0 % ; and (iv) w r o n g o r unsatisfactory a n s w e r s (reliability i n t e r v a l 0.4). 4 test e x a m p l e s , o r a b o u t 12%. I n Fig.6 the o u t c o m e of t h i s LS i s s h o w n o n : a) m a i n m e n u , b)problem d e f i n i t i o n , c)knowledge base e d i t o r , and d)ES answer with the reasoning l i n e explanation. G i v e n answer relates to conceptual diagnosis and m a i n t e n a n c e , a s w e l l a s t o required s p a r e parts. For working s t a t i o n MC DC-30 the k n o w l e d g e base has been formed o n the basis o f t h e translation of t h e existing diagnostic s y s t e m into f a c t s and rules, w h i l e f o r I R B 6 / 2 ASEA t h e f o r m a l i s a t i o n of e l e c t r o n i c e r r o r s i n Lhe k n o w l e d g e base r u l e s w a s made. Finally, t h e k n o w l e d g e base w a s formed for UMC-850 c o n t a j n i n g quality i n d i c e s o f t h i s ME1 (kinematic a c c u r a c y , measuring u n c e r t a i n t y , g e o m e t r i c a l accuracy of a x e s and Lhe measuring o f e t a l o n pieces). In this e x a m p l e EXNAS i s used a s ES consultant f o r establishing o f checking procedures f o r corresponding quality parameters. T h e o u t p u t f r o m this ES for a l l described e x a m p l e s with detailed comment of r e s u l t s i s given in [ a ] . 4.
CONCLUSlON T h e described ES model has been developed for off-line approach to t h e d i a g n o s i s and m a i n t e n a n c e o f FMS working s t a t i o n s , Fig.7, with the f o l l o w i n g possibilities of industrial application: (i) t h i s model may be applied with the u s e o f PC c o m p u t e r s for c e r t a i n work s t a t i o n s , (ii) with i n s t a l l i n g o f a corresponding A 1 language i n t o t h e c o m p u t e r control u n i t , i t w i l l be possible to translate the existing diagnostic s y s t e m into t h e expert d i a g n o s t i c system according to t h e EXXAS m o d e l , a s i t h a s been done for MC DC-30 and IRB 6 1 2 . T h e f u t u r e r e s e a r c h o n t h i s ES will i n c l u d e : (i)the research and improveof ment o f ES basic features,such a s : (a)testing established concept o f t h e k n o w l e d g e base and inference e n g i n e on a l a r g e c o m p u t e r , with d i f f e r e n t languages and A1 t o o l s , (b)the research o f o p t i m a l models f o r t h e representation o f different k n o w l e d g e , and in particular of c o g n i t i v e p r o c e s s o f m a i n t e n a n c e e n g i n e e r s , (c)the research o f i n f e r e n c e e n g i n e model with distributed r e a s o n i n g , and (d)the d e v e l o p m e n t o f a learning m o d e l , orinted to s p e c i f i c problems in d i a g n o s i s and maintenance o f divergent FMS work s t a t i o n s , and (ii)the research o f problems r e l a t i n g to t h e ES e n v i r o n m e n t , these being: (a)occurrences and diagnostic processes o n d i f f e r e n t FMS work s t a t i o n s , (b)CIM c o n c e p t , t h e p l a c e , t h e role and t h e integration of ES with t h e s o f t w a r e f o r t h i s c o n c e p t , and (c) t h e development o f models o f a d a p t i b l e e x p e r t .. aiagnostic systems
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5. REFERENCES Kobayashi. K., Inaba,S.,1985, T h e P r a c t i c a l Appr o a c h t o a n Unmanned FMS O p e r a t i o n , F l e x i b l e Automation in Japan, IFS (publications),Ltd., Bedford. 2. Y o s h i k a w a , H . , 1 9 7 7 , A Preliminary S t u d y o f Diagn o s i s and Elaintenance o f ?lanufacturing S y s t e m s , N T H S I N T E F , Trondheim. 3. S h a h i n p o o r , PI., V e l l s , D . , 1 9 8 6 , Engineering D i a g n o s t i c s and Troublesl~ooting: A New U s e f o r Fuzzy Logics, C l a r k s o n C o l l e g e o f Technology, Post dam. 4 . T a k a t a , S . , e t a l l , 1 9 8 5 , Monitoring and D i a g n o s i s System o f Machine T o o l s , Department of P r e c i s i o n E n g i n e e r i n g , UniversiLy of T o k y o , Tokyo. 1.
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5 . M o n o s t o r i , L . , 1986, L e a r n i n g P r o c e d u r e s i n M a c h i n e T o o l N o n i t o r i n g , C o m p u t e r s i n I n d u s t r y , No. 7,pp.53-64. 6. Weck, e t a l l , 1984, U n i v e r s e l l e s S y s t e m z u r P r o z e s und Anlagenubermachung, L a b o r a t o r i u m f u r Werkzeugmaschinen und B e t r i e b s s l e h r e der RWTH A a c h e n , Aachen. 7 . Mi.laci.c, V . , Plajstorovic, V., 1987, The Future of Computerised Maintenance, I F I P Working Conference "Diagnostic and Preventive Maintenance Strategies i n Manufacturing Systems",Dubrovnik. 8. M a j s t o r o v i c , V . . 1988, T h e DevelopmenL o f the &S M o d e l f o r D i a g n o s i s a n d : . l a i n L e n a n c e o f FMS w o r k i n g s t a n t i n n n . PhD t h e s i s , E l e r h n n i r n l E n g i neering Faculty, Beograd University. 9. M a j s t o r o v i c . V . , 1987, E x p e r t S y s t e m f o r C o m p u t e r I n t e g r a t e d M a i n t e 11a n c e , P r o c e e d i n 8 o f I n t e r n a t i o nal Conference: "Elodern Concepts and Methods i n Maintenance", London. l O . N i l a c i c , V . , Plajstorovic, V . ,1988, The Building of t h e K n o u l e d g e B a s e Model f o r C o m p u t e r I n t e g r a t e d M a i n t e n a n c e S y s t e m , P r o c e e d i n g s of I n t e r n a t i o n a l C o n f e r e n c e : " H a n u f a c t u ring International", Atlanta. 1 1 . Waterman, D . , 1 9 8 6 , A G u i d e t o E x p e r t S y s t e m s , A d d i s o n W e s l e y P u b l i s h i n g Company, E l a s s a c h u s e t t s . lZ.Hilacic, V. ,1987, Manufacturing Systems IIIManufacturing Systems Design Theory, Faculty of Mechanical Engineerjng, Beograd (in SerboCroatian). 1 3 . A l e x a n d e r , J . , H a n n a , F.. 1 9 7 8 , A u t o m a t a T h e o r y : An E n g i n e e r i n g A p p r o a c h , E d w a r d A r n o l d , L o n d o n .
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DESCRIPTION
COMPONENTS CDNNCT~ONS
SPARE M T S OESCRIPTON
mOOuCTION
MAINkNANCE MODELS
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FOR MAIMENANCE PLANNER
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CLWFARATIVF ANALYSIS
SMLATION
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MINI MACHINE 2 MC DC- 30 MINI MACHINE 3ASEA IR8 6 / 2
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MINI MACHINE 5MC HBG eo
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FG. 1. BWCK MOM OWELOPING DUGNOSK E X m T SYSTEM M p
MINI MACHINE 6MC HBG eo
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FIG. 4. AUTOMATA CHARACTERISTICS WITW
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FIG.2. EXPERT SYSTfM R)R MAINTtNMCE It-k'MSI
- BASIC
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STRUCTURE
491
C O M M E N T
a) SUGGESTED E X M A S
choice Expert System for Conceptual Diagnosis and Maintenance for FMS Working Stations -LOCATION-BEARING-with probability 0 . 4 2 2 Y O 5 4 7 (Prototype. Version 1.0)
I think so because: The question-ARE-VIRRATIONS_INCREASED-answered:5, EXMAS
-
MAIN W N U : Thc que.stion-IS-TEMPERATURE-HIGHER-answered: yes
1.
PROBLEM D6FINITION
2.
KNOWLEDGE BASE EDITOR
Against so formulated opinion are the facts that: The question- IS-MAIN-SPINDLEPROTATINf;-answered: no 2 . 1.
READ The forming
of
the
opinion
about
this
choice not
2.2. APPEND
influenced by: 2 . 3 . DELETE
The question-IS-NOISE-INCREASED-answered:O 2.4.
PRINT Remark: Other choices less probable.
2.5. GO BACK
Yes 3.
ACTION
4.
EXIT
?-ma1ntenance-techno1ogy . For diagnosis BEARING-LOCATION
additional maintenance
technology is:
b) 1,
TAKE_DOWN-BEARING-ANDEXCHANGBD_DAMAC;ED-DAMAGED-ELEMENT, PROEiLEM DEFINITION
while spare parts are:
For diagnosis position MAIN-SPINDLE probability
OUTSIDE~RING-NUM~ERPlOOOOl-*~INTERNAL~RING~NIJMBER~
is 0.1;
100002~BALL~NUMBER~1OOOO~~i~HOLDER~NUMBER~lOOOO4
-
IF position main-spindle THEN 0.92 0.3 noise;
yes
-
FOR symptom-VIBR question is considerable;
?-halt.
-
4 2.
FIG.6. EXMAS - B A S I C FEATURES a) Main M e n u ; b ) Problem D e f i n i t i o n ; c ) Knowledge Base Editor; d ) E X M A S ' answer
KNOWLEDGE BASE EDITOR IF
-
locat ion-main-spindle
THEN
maintenance
technology:diagnostic-programs_SJ37-e~8tabli~h diagnosis-fiinction-spindle-then-consult-maintenance-mnual-chapter-lO-*-l1_i-12;
-
1-1
IF location-main-spindle THEN spare parts are:
-0MGNOSK
id-number-fro~catalogue_r/d_045604_+-600504-
TiCHWLOoI
i-5006500-*-500069; d) 3.
ACT I ON ?-execute-diagnosic. IS_BlJTTON-"TOOL-NOT-CLAMPED"-AI.IGHT: no.
! I
IS~THERE~NO~CLEANINGOF_MAIN_SPI"O_AIR~:no.
IS-MAIN-SPINDLE-ROTATING: no. ARE-VIBRATIONS-INCREASED: 5. IS-ROTATION-UBBVEN: 0. IS-NOISE-INCREASED: 0 . IS-TEMPERATURE-HIGHER: yes.
Suggested choice (i): LOCATION-BEARING with probability 0 . 4 2 2 ' 3 0 5 4 7 yes ?-why.
492
-1NTiR FACi CNC CU
KEY WORDS: Diagnosis, Expert Systems. F>IS, Maintenance.
1. THE APPROACHES TO THE DEVELOPMENT OF DIAGNOSTIC SYSTEHS - RESEARCH PROBLEM DEFINITION FMS to-day represent t h e highest level of f l e x i b l e automation widespread in industrial environment. These a r e computerised plants with high level data processing distribution and the automatic f l o w o f material, consisting from different mechatronic working stations. T h e development o f t h e concept o f “un-manned” machining s t a t i o n s gave the basic i m p u l s e for the development of diagnostic s y s t e m s a t a l l technoloeic a l levels in flexible manufacture. Kobayashi [ I ] attaches t h e greatest importance for t h e development of this concept t o t h e following three factors: the f l e x i b i l i t y , reliability and diagnostic system. T h e theoretical f u n d a m e n t s of diagnostic s y s t e m s for manufacturing s y s t e m s have been s e t down by prof. Yoshikava [2]. T h e more recent characteristic approaches include: t h e use o f fuzzy s e t s [3], c a u s a l model o f working s t a t i o n s [4] and the u s e of A1 in the s e l e c t i o n of diagnostic s i g n a l s [5,61. The diagnostic system o f working s t a t i o n s i s developed o n theoretical and experimental a n a l y s i s of i t s processes and a p p e a r a n c e s , Fig.1. W i t h s u c h working s t a t i o n diagnostic model the d i a g n o s i s i s made on t h e basis o f comparative a n a l y s e s o f real behaviour and simul.ated diagnostic parameters. T h i s paper has two parts. T h e fj.rsr: part describes developed EXNAS model i l l d e t a i l . The second p o r t presents some r e s u l t s o f practical tests.
2. EXNAS M O D E L During FElS working s t a t i o n lifecycle three s t a g e s may be idenlified: d e s i g n i n g , production and exploitation. T h e first t w o , a s a r u l e , relate to t h e producers and t h e third o n e to users.For this concept a multilevel mainLenance m o d e l has been developed which d e f i n e s the engineering a p p r o a c h e s to: (i)maintenance d e s i g n , (ii)maintenance technology d e s i g n , a n d (iii)mainLenance planning and c o n t r o l [ O ] . T h e model i s based o n a mechanical s y s t e m , and higher l e v e l s relate t o the a b o v e stated g r o u p s o f engineering activites (71. The entire model, the same a s each stratified level, has i t s o w n inputs. o u t p u t s and c o n n e c t i o n s with t h e superior-subordinated l e v e l s [ l o ] . ‘These information f l o w s were t h e basis f o r t h e building of EXNAS model whose principal architecture i s s h o w n in F i g . 2 . It h a s t h e following entities: (i)ES s h e l l (the k n o w l e d g e base a n d inference engine). (ii)the processor and (iii.)the communication i.nterface. T h e physical organisation of the working s t a t i o n i s a starting element: for the description of machine structure and thc determining of technical e f f e c t i veness parameters. T h e c o m p o n e n t s , mutually i n t e r connected with their mechanical, e l e c t r i c a l , thermal or other i n f l u e n c e s may prevent the failure of other components, which i s then defined with the k n o w l e d g e engineering p r o c e d u r e s o n t h e basis o f c o n n e c t i o n s and relations. T h i s may e n a b l e r e l i a b l e establishing o f d i a g n o s i s both for t h e condition-based maintenance technology and c o r r e c t i v e maintenance. T h i s approach is known a s c a u s a l model o r t h e first-principle based reasoning [ll]. T h e second a p p r o a c h , the heuristic reasoning i s s u i t a b l e for t h e representation of heuristic k n o w l e d g e , the description of designere n g i n e c r ’ s and maintenance p l a n n e r ‘ s cognitive processes. T h i s is particularly important in the appearo n c c o f n e w failures or s y m p t o m s , rrh1r.h s e r v e with the existing heuristic k n o w l e d g e about t h e system for establishing o f partial o r f u l l diagnosis. T h i s approach is t h u s s u i t a b l e when i t i s not possible to represent the c o m p l e t e k n o w l e d g e a b o u t m a c h i n e ‘ s
Annals of the ClRP Vol. 38/1/1989
s t r u c t u r e , i t s f u n c t i o n s and features. O u r research s h o w s that thc combined u s e o f both a p p r o a c h e s i s very convenient. f o r FMS working s t a t i o n s , which h a s been d o n e in the d e v e l o p m e n t of EXMAS. For t h e inference e n g i n e m o d e l , a s t h e second vital EXMAS module, he following p r i n c i p l e was a p p l i e d : f o r the model o f c o n c e p t u a l d e f i n i t i o n o f t h e condition-based maintenance t e c h n o l o g y , f o r w a r d c h a i n i n g , and for corrective maintenance technology back c h a i n i n g w a s used. T h e processor i s t h e second EXMAS e n t i t y , which i s f i l t e r i n g inference e n g i n e c o n c l u s i o n s through: (i)recogniLion procedures for d i a g n o s i s a n d maintenance (cases and processes). (iilenables t h e realis a t i o n o f corresponding logic o f general and s p e c i f i c c h a r a c t e r f o r the subject a r e a , and (iii)optimises c o r r e s p o n d i n g s o l u t i o n s o n the basis of o p t i m i s a t i o n c r i t e r i a , a c c o r d i n g to f u n c t i o n s , s t a t e s , processes and g o a l s . T h e diagnosis l o g i c o f working s t a t i o n s is based o n the research and modelling o f d i f f e r e n t above stated appearances and p r o c e s s e s , w h i l e t h e maintenance technology logic i s based o n : (i)results of d i a g n o s t i c process, and (ii)applied m o d e l s of maintenance technology. T h e economy and optimisation model o t d i a g n o s i s and maintenance r e l a t e s t o he procedures o f t h e maintenance cost optimisation and spare parts s t o r e s , he maintenance planner. appearing in The f u n c t i o n a l knowledge structure in EXMAS may be expressed with the following equation:
F S ~ = (F R ,SB,OB,S, K ) (1) where:FB-functional knowledge blocks, the most important f o r t h i s E S model being those related to different recognition classes, SB-structural blocks representing the k n o w l e d g e and relations in t h e s y s t e m of h i e r a r c h y , OB-operation k n o w l e d g e block b y which conclusion strategy is e s t a b l i s h e d , S coupling f u n c t i o n by which k n o w l e d g e acquisition model i s a c h i e v e d , and K-composition f u n c t i o n by which t h e reasoning model in the k n o w l e d g e base o f this ES i s established. T h e e n t i t i e s of d e c l a r a t i v e and heuristic k n o w l e d g e for the maintenance planner a r e represented through the s t r u c t u r e o f f a c t s and rules in Fig.3. T h e established model of the functional k n o w l e d g e s t r u c t u r e i s t h e basi., for finding c o r r e s p o n d i n g l a n g u a g e with g r a m m a r , and l o r the c o n s t r u c t i o n of a u t o m a t a [ 1 2 , 1 3 ] , s o that the k n o w l e d g e transformation h a s the form:
FS-G-A
(2)
where:L(G)-the language by which the transformation of f u n c t i o n k n o w l e d g e structure (FS) into s u i t a b l e grammar ( G ) is a c h i e v e d , and A-automaton c o r r e s p o n d ing t o t h e selected language and t h e c o r r e s p o n d i n g grammar. I n t h i s expert system t h e engineering logic i s automata-modelled, but oriented t o e n g i n e e r i n g logic with s o m e empiric c o n t e n t s also. T h e f o l l o w i n g s i t u a t i o n s in t h e diagnosis and maintenance a r e modelled with automat0n:maintenance s t r u c t u r e ; r e l a tions: symptoms-diagnoses, re1ations:diagnostic c o n clusions-maintenance t e c h n o l o g y , e t c . T h i s h a s been achieved with nondeterminate a u t o m a t o n in the form o f a q u i n t u p l e (input.starting s t a t e , transfer f u n c t i o n , s t a t e s and tinal state). W i t h linking o f executivii rules t h e c h a i n s a r e defined f o r t h e k n o w l e d g e acquis i t i o n and inference line. F i g . 4 s h o w s summarised a n a l y s i s o f r e s u l t s obtained with the s o f t w a r e s u p p o r t for a u t o m a t a , maintenance computer s t r u c t u r e s and selected mini machines in the “pilot“ FMS model. T h e l a r g e number o f reasoning l i n e s from 1 2 to 1 7 2 i s
particularly stressed. T h e y e n a b l e the realisation of d i f f e r e n t inference e n g i n e conclusions.
3 . E X M A S - SOME RESULTS OF EXPERIMENTS T h e r e l a t i o n s between certain k n o w l e d g e entities for maintenance technology d e s i g n e r , i.e. a psrt of t h e maintenance p l a n n e r , relating to maintenance planning o f work s t a t i o n s a r e shown in Fig.5. F o n terminate s y m b o l M M represents d i f f e r e n t work station maintenance s t r u c t u r e s , a n d the f a c t s representing declarative k n o w l e d g e . T h e structure and connections for a given mechanical s y s t e m a r e idenLified with the basic rules. T e r m i n a t e s y m b o l a i s defined as:a = (elements, features. quantity), 'and t h e followiAg o p e r a t i o n s a r e carried out over t h i s knnw1Pdgr:a = ( d e c o m p o s i t i o n , c o u p l i n g ) . T h e decomposition operahicns relate to t h e establishlng of the e n t i r e maintenance s t r u c t u r e o f work station,and the coupling i s carried out a c c o r d i n g L O r e l a t i o n s h i p r e l a t i o n s in maintenance s t r u c t u r e in question. T h e formalisaLion of t h i s k n o w l e d g e is made according to the r u l e s with the f o l l o w i n g form: S T R U C T U R E COMPONENTS: : =ELENENT (e1ement)IS-PART-OF(e1ement);STRUCTUKE CONNECTIONS::= E L E I I I E N T ( e l e m e n t ) I S _ A T O E l ( a t o m ) . These three g r o u p s of rules i n P R O L O G notation describe a l l possible f o r m s of t h e s t r u c t u r e o f t h e work station,including: hierarchical s t r u c t u r a l e l e m e n t s , c o n n e c t i o n s and i t s last element. O n the basis of similar hypotheses the d e f i n i t i o n and the formalisation of the k n o w l e d g e for other nonterminate and terminate s y m b o l s o f Lhe graph i n question a r e made. T h e problem of sched u l i n g o f planned maintenance i n WRP production c o n t r o l c o n c e p t in FNS. Fig.3. i s c o m p l e x , f o r which the question o f priorities between planned and corr e c t i v e maintenance has to be s o l v e d . In (81 detailed model i s presented. Here characteristic examples a r e quoted for t h e group o f strategic r u l e s , T h e first example i s : R U L E 4 (Priotity 2 for f a i l u r e removal); IF failure a t work s t a t i o n prioty 2 THEN apply Rule 4 on planned maintenance o p e r a t i o n s with probability o f 0 . 8 . T h e maintenance technology s c h e d u l i n g f o r FMS work s t a t i o n s i s made a c c o r d i n g t o t h r e e criteria: maintenance operations, maintenance work o r d e r s and mechanical s y s t e m t o be maintained. T h e strategic r u l e s f o r scheduled maintenance o p e r a t i o n s a r e classified i n t o f i v e groups. Example: R U L E 2 , I F priority 1 (certainty factor - 0 . 8 ) T H E N maintenance operations planned f o r the most s u i t a b l e production terms. Scheduling o f maintenance repair o r d e r s i s carried out a c c o r d i n g t o criteria for 9 priorities. Example: RULE 3 ; IF priority 2 (certainty factor - 0 . 8 ) THEN carry out work orders with t h e highest downtime c o s t s per unit of time. T h e third group o f rules rel a t e s t o the definition o f priorities for mechanical systems. T h e r e a r e 5 in a l l : RULE 1 ; IF priority 0 (certainty factor -0.9) T H E F mechanical s y s t e m s very complex. T h e failure o n them c a u s e s very g r e a t damage Translated i n t o the e n t i r e logic o f t h i s expert SYStem, f o r n o n t e r m i n a t e node-scheduling ( S C H ) f o u r l i n e reasoning may be established: PIS+ a7 a2 aJ dl d2 , Y S + a 7 bl d7 d2 , MS+ cr c 2 d4 d7 dg and EIS + f, d4 d, d 2 . T h e first reasoning l i n e relates to scheduling according to m a i n t e n a n c e operations, a n d t h e third and the fourth according to t h e maintenance history and the c o n t r o l o f spare part s t o r e s (maintenance orders) In the f u r t h e r text t h e e x a m p l e s o f r u l e s for maintenance planner f o r t h e work s t a t i o n PIC HBG80 a r e given. First two basic rules for nonterminate s y m b o l -DP a r e given, Fig.3. M C a s machining station has c e r t a i n quality parameters relating to t h e geomet r i c a l , k i n e m a t i c and operation a c c u r a c y , s o that the g e n e r a l form o f t h e rule for quality diagnosis parameters may be written in the following form: IF (quality f u n c t i o n ) ( p a r a m e t e r s ) T H E N ( e x a m i n a t i o n procedure). T h e e x a m p l e is: RULE 1 8 (quality diagnosis);IF parallelity o f machined s u r f a c e s A and B in housing No.016845 i s under 0.025 mm THEN establish parallelity o f main s p i n d l e i n relation to x-axis according to DIN 8620. For identification o f f a i l u r e t h e general f o r m o f t h e rule is: IF ( f a i l u r e - o c c u r r e n c e ) T H E N (place.with probability pi)and t h e e x a m p l e is:RULE 28; IF a f t e r completion o f machining c y c l e tool holder not released from main s p i n d l e THEN evidence suggested with probability 0 . i 5 .that failure o n lead-out valve o f pneumatics of tool c l a m p i n g system. Given r u l e s for condition based maintenance technology h a v e a c o m m o n form: IF (machine element) THEN (maintenance technology)(parameters). Example: IF front main s p i n d l e bearing of MC HBG-80 THEN exchange bearing when: (i)vibration amplitude a t measuring s i t e 1 during two successive mesurements 0 . 0 8 mm and 1 during two (ii)temperature a t measuring site s u c c e s s i v e measurements 85'C. T h e r e l a t i o n s between FMS c h a r a c t e r i s t i c s a r e defined with s t r a t e g i c rules applied maintenance technology m o d e l s , and procedures for s p a r e part storage control. E x a m p l e o f rule for s e l e c t i o n and application o f diagnostic procedures: R U L E 5 ; IF (i)main s p i n d l e o n c o n d i t i o n based maintenance and (ii)there e x i s t r u l e s with mention
of VIBRATIONS, and (iii)Lhere exist rules with word T E M P E R A T U R E THEN suggested evident (0.75) that r u l e s relating t o VIBRA'PIONS should be executed before rules with word TEMPERATURE. T h e presented ES model w a s tested a t the a b o v e mentioned working station. T h e k n o w l e d g e base for the diagnostic s y s t e m for ZIC HBG-80 w a s formed in PROLOG notation by forming t h e following f a c t s and rules: f a i l u r e occurrence probability, symptoms, definition of connections: diagnosis(e)-symptom ( u ) ~element (u)-technology(ej o f c o r r e c t i v e maintenance and element (u)-spare part (parts). T h e chaining i n the k n o w l e d g e base i s achieved with t h e u s e of PROLOG f e a t u r e s , backward r e t r i e v a l , when i n interactive dialogue, ES answers, corresponding f a c t s a b o u t the symptom a r e entered for t h e c h a i n i n g of hypotheses. T h e c o n c l u s i o n s a r e d r a w n via c e r t a i n t y f a c t o r , calculated with B a y e s formula.Conflicting s i t u a t i o n s a r e solved with selectivity c o e f f i c i e n t s , representing the s u m o f Bays probability r e l a t i o n s o i the e v e n t : s y m p t o m - d i a g n o s i s . I n order to check t h e basic f e a t u r e s of developed ES, i t w a s tested o n 39 different e x a m p l e s f o r t h e above mentioned MC. T h e folowing r e s u l t s w e r e o b tained f r o m a n s w e r s received and e x p l a n a t i o n s g i v e n by reasoning lines: (i) absolutely a c c u r a t e a n s w e r s : 10 t e s t e x a m p l e s o r a b o u t 2 5 X ; (ii) e x a c t a n s w e r s 17 test (answer reliability interval:0.9-0.6), e x a m p l e s o r 4 5 % ; (iii)partly exact ansuers.requiring additional a n a l y s e s (reliability interval:0.4-0.6). 8 t e s t e x a m p l e s , o r a b o u t 2 0 % ; and (iv) w r o n g o r unsatisfactory a n s w e r s (reliability i n t e r v a l 0.4). 4 test e x a m p l e s , o r a b o u t 12%. I n Fig.6 the o u t c o m e of t h i s LS i s s h o w n o n : a) m a i n m e n u , b)problem d e f i n i t i o n , c)knowledge base e d i t o r , and d)ES answer with the reasoning l i n e explanation. G i v e n answer relates to conceptual diagnosis and m a i n t e n a n c e , a s w e l l a s t o required s p a r e parts. For working s t a t i o n MC DC-30 the k n o w l e d g e base has been formed o n the basis o f t h e translation of t h e existing diagnostic s y s t e m into f a c t s and rules, w h i l e f o r I R B 6 / 2 ASEA t h e f o r m a l i s a t i o n of e l e c t r o n i c e r r o r s i n Lhe k n o w l e d g e base r u l e s w a s made. Finally, t h e k n o w l e d g e base w a s formed for UMC-850 c o n t a j n i n g quality i n d i c e s o f t h i s ME1 (kinematic a c c u r a c y , measuring u n c e r t a i n t y , g e o m e t r i c a l accuracy of a x e s and Lhe measuring o f e t a l o n pieces). In this e x a m p l e EXNAS i s used a s ES consultant f o r establishing o f checking procedures f o r corresponding quality parameters. T h e o u t p u t f r o m this ES for a l l described e x a m p l e s with detailed comment of r e s u l t s i s given in [ a ] . 4.
CONCLUSlON T h e described ES model has been developed for off-line approach to t h e d i a g n o s i s and m a i n t e n a n c e o f FMS working s t a t i o n s , Fig.7, with the f o l l o w i n g possibilities of industrial application: (i) t h i s model may be applied with the u s e o f PC c o m p u t e r s for c e r t a i n work s t a t i o n s , (ii) with i n s t a l l i n g o f a corresponding A 1 language i n t o t h e c o m p u t e r control u n i t , i t w i l l be possible to translate the existing diagnostic s y s t e m into t h e expert d i a g n o s t i c system according to t h e EXXAS m o d e l , a s i t h a s been done for MC DC-30 and IRB 6 1 2 . T h e f u t u r e r e s e a r c h o n t h i s ES will i n c l u d e : (i)the research and improveof ment o f ES basic features,such a s : (a)testing established concept o f t h e k n o w l e d g e base and inference e n g i n e on a l a r g e c o m p u t e r , with d i f f e r e n t languages and A1 t o o l s , (b)the research o f o p t i m a l models f o r t h e representation o f different k n o w l e d g e , and in particular of c o g n i t i v e p r o c e s s o f m a i n t e n a n c e e n g i n e e r s , (c)the research o f i n f e r e n c e e n g i n e model with distributed r e a s o n i n g , and (d)the d e v e l o p m e n t o f a learning m o d e l , orinted to s p e c i f i c problems in d i a g n o s i s and maintenance o f divergent FMS work s t a t i o n s , and (ii)the research o f problems r e l a t i n g to t h e ES e n v i r o n m e n t , these being: (a)occurrences and diagnostic processes o n d i f f e r e n t FMS work s t a t i o n s , (b)CIM c o n c e p t , t h e p l a c e , t h e role and t h e integration of ES with t h e s o f t w a r e f o r t h i s c o n c e p t , and (c) t h e development o f models o f a d a p t i b l e e x p e r t .. aiagnostic systems
~~
5. REFERENCES Kobayashi. K., Inaba,S.,1985, T h e P r a c t i c a l Appr o a c h t o a n Unmanned FMS O p e r a t i o n , F l e x i b l e Automation in Japan, IFS (publications),Ltd., Bedford. 2. Y o s h i k a w a , H . , 1 9 7 7 , A Preliminary S t u d y o f Diagn o s i s and Elaintenance o f ?lanufacturing S y s t e m s , N T H S I N T E F , Trondheim. 3. S h a h i n p o o r , PI., V e l l s , D . , 1 9 8 6 , Engineering D i a g n o s t i c s and Troublesl~ooting: A New U s e f o r Fuzzy Logics, C l a r k s o n C o l l e g e o f Technology, Post dam. 4 . T a k a t a , S . , e t a l l , 1 9 8 5 , Monitoring and D i a g n o s i s System o f Machine T o o l s , Department of P r e c i s i o n E n g i n e e r i n g , UniversiLy of T o k y o , Tokyo. 1.
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5 . M o n o s t o r i , L . , 1986, L e a r n i n g P r o c e d u r e s i n M a c h i n e T o o l N o n i t o r i n g , C o m p u t e r s i n I n d u s t r y , No. 7,pp.53-64. 6. Weck, e t a l l , 1984, U n i v e r s e l l e s S y s t e m z u r P r o z e s und Anlagenubermachung, L a b o r a t o r i u m f u r Werkzeugmaschinen und B e t r i e b s s l e h r e der RWTH A a c h e n , Aachen. 7 . Mi.laci.c, V . , Plajstorovic, V., 1987, The Future of Computerised Maintenance, I F I P Working Conference "Diagnostic and Preventive Maintenance Strategies i n Manufacturing Systems",Dubrovnik. 8. M a j s t o r o v i c , V . . 1988, T h e DevelopmenL o f the &S M o d e l f o r D i a g n o s i s a n d : . l a i n L e n a n c e o f FMS w o r k i n g s t a n t i n n n . PhD t h e s i s , E l e r h n n i r n l E n g i neering Faculty, Beograd University. 9. M a j s t o r o v i c . V . , 1987, E x p e r t S y s t e m f o r C o m p u t e r I n t e g r a t e d M a i n t e 11a n c e , P r o c e e d i n 8 o f I n t e r n a t i o nal Conference: "Elodern Concepts and Methods i n Maintenance", London. l O . N i l a c i c , V . , Plajstorovic, V . ,1988, The Building of t h e K n o u l e d g e B a s e Model f o r C o m p u t e r I n t e g r a t e d M a i n t e n a n c e S y s t e m , P r o c e e d i n g s of I n t e r n a t i o n a l C o n f e r e n c e : " H a n u f a c t u ring International", Atlanta. 1 1 . Waterman, D . , 1 9 8 6 , A G u i d e t o E x p e r t S y s t e m s , A d d i s o n W e s l e y P u b l i s h i n g Company, E l a s s a c h u s e t t s . lZ.Hilacic, V. ,1987, Manufacturing Systems IIIManufacturing Systems Design Theory, Faculty of Mechanical Engineerjng, Beograd (in SerboCroatian). 1 3 . A l e x a n d e r , J . , H a n n a , F.. 1 9 7 8 , A u t o m a t a T h e o r y : An E n g i n e e r i n g A p p r o a c h , E d w a r d A r n o l d , L o n d o n .
STRUURE
F&
TEChWOlOGIK
DESCRIPTION
COMPONENTS CDNNCT~ONS
SPARE M T S OESCRIPTON
mOOuCTION
MAINkNANCE MODELS
GRAPH
P1ANNING
Mrnm
FOR MAIMENANCE PLANNER
~1 STRAKGK RU1fS
I
a4iu
GIVEN RULES
RU(ES
I
LOCATION
I
r
Fffi.3 FACTS AN0 RULCS FOR UAINTENANCE PLANNCR STRUCTUM
THEORt7lCAL ANALYSIS EXPtRrrCNTAl ANAllYS
I
MOOEL OF WORKING STA TlON
CLWFARATIVF ANALYSIS
SMLATION
I
J
3
MINI MACHINE 1-
NCMV
mc- eso
6 10
MINI MACHINE 2 MC DC- 30 MINI MACHINE 3ASEA IR8 6 / 2
t
3
I
I
-
I
4
4 12
MINI MACHINE 5MC HBG eo
-
FG. 1. BWCK MOM OWELOPING DUGNOSK E X m T SYSTEM M p
MINI MACHINE 6MC HBG eo
-
I
4
I
8 4
1
4 10 _. 3
48
4
-
32
4
5
7
3
2
2
2
4 12
I
6
MINI MACHINE 4 NCMM UMC- 850
-
12
5
<
6
172 10
(2 170
S
FIG. 4. AUTOMATA CHARACTERISTICS WITW
I USCR
I I
-1
INPUT
INTfRFWTER
FIG.2. EXPERT SYSTfM R)R MAINTtNMCE It-k'MSI
- BASIC
I
STRUCTURE
491
C O M M E N T
a) SUGGESTED E X M A S
choice Expert System for Conceptual Diagnosis and Maintenance for FMS Working Stations -LOCATION-BEARING-with probability 0 . 4 2 2 Y O 5 4 7 (Prototype. Version 1.0)
I think so because: The question-ARE-VIRRATIONS_INCREASED-answered:5, EXMAS
-
MAIN W N U : Thc que.stion-IS-TEMPERATURE-HIGHER-answered: yes
1.
PROBLEM D6FINITION
2.
KNOWLEDGE BASE EDITOR
Against so formulated opinion are the facts that: The question- IS-MAIN-SPINDLEPROTATINf;-answered: no 2 . 1.
READ The forming
of
the
opinion
about
this
choice not
2.2. APPEND
influenced by: 2 . 3 . DELETE
The question-IS-NOISE-INCREASED-answered:O 2.4.
PRINT Remark: Other choices less probable.
2.5. GO BACK
Yes 3.
ACTION
4.
EXIT
?-ma1ntenance-techno1ogy . For diagnosis BEARING-LOCATION
additional maintenance
technology is:
b) 1,
TAKE_DOWN-BEARING-ANDEXCHANGBD_DAMAC;ED-DAMAGED-ELEMENT, PROEiLEM DEFINITION
while spare parts are:
For diagnosis position MAIN-SPINDLE probability
OUTSIDE~RING-NUM~ERPlOOOOl-*~INTERNAL~RING~NIJMBER~
is 0.1;
100002~BALL~NUMBER~1OOOO~~i~HOLDER~NUMBER~lOOOO4
-
IF position main-spindle THEN 0.92 0.3 noise;
yes
-
FOR symptom-VIBR question is considerable;
?-halt.
-
4 2.
FIG.6. EXMAS - B A S I C FEATURES a) Main M e n u ; b ) Problem D e f i n i t i o n ; c ) Knowledge Base Editor; d ) E X M A S ' answer
KNOWLEDGE BASE EDITOR IF
-
locat ion-main-spindle
THEN
maintenance
technology:diagnostic-programs_SJ37-e~8tabli~h diagnosis-fiinction-spindle-then-consult-maintenance-mnual-chapter-lO-*-l1_i-12;
-
1-1
IF location-main-spindle THEN spare parts are:
-0MGNOSK
id-number-fro~catalogue_r/d_045604_+-600504-
TiCHWLOoI
i-5006500-*-500069; d) 3.
ACT I ON ?-execute-diagnosic. IS_BlJTTON-"TOOL-NOT-CLAMPED"-AI.IGHT: no.
! I
IS~THERE~NO~CLEANINGOF_MAIN_SPI"O_AIR~:no.
IS-MAIN-SPINDLE-ROTATING: no. ARE-VIBRATIONS-INCREASED: 5. IS-ROTATION-UBBVEN: 0. IS-NOISE-INCREASED: 0 . IS-TEMPERATURE-HIGHER: yes.
Suggested choice (i): LOCATION-BEARING with probability 0 . 4 2 2 ' 3 0 5 4 7 yes ?-why.
492
-1NTiR FACi CNC CU