R ULE -B ASED K NOWLEDGE R E P R E S E N T AT I O N
R ULE -B ASED K NOWLEDGE R E P R E S E N T AT I O N
Rule-based knowledge representation defines how the expert’s knowledge is used to make a decision. The rules used are either antecedent (IF something happens) or consequent (THEN take this action). For example, to the question, What causes the volume in the tank to drop?, the expert may respond with the answer, a malfunctioning tank system. The knowledge
Industrial Text & Video Company 1-800-752-8398
www.industrialtext.com
S ECTION Advanced PLC Artificial Intelligence C HAPTER 5 Topics and Networks
and PLC Systems 16
engineer may implement this information as the following rule: IF the volume is less than the set point, THEN annunciate a system malfunction due to a loss of volume.
Rules can be as long and complex as needed for the process, and they usually define the involvement of the AI system. For instance, a simple rule-based system (few rules, not very complex) may formulate a simple diagnostic rule, such as:
IF the temperature is less than the set point, THEN open the steam valve
A more complex diagnostic formula would involve rules that depend on parent rules:
IF case 1, THEN ELSE nothing IF case 2, THEN ELSE something
IF case 3 THEN nothing
where each of the case conditions represents a particular measurement, comparison, or situation. Figure 16-2 illustrates a decision tree for forming AI rules.
Figure 16-2. Decision tree.
Industrial Text & Video Company 1-800-752-8398
www.industrialtext.com
S ECTION Advanced PLC Artificial Intelligence C HAPTER 5 Topics and Networks
and PLC Systems 16
A slightly different degree of complexity occurs in a rule-based knowledge representation when the rule has several probable causes. For example:
valve failure or
IF volume drops, THEN pump failure
or feedback failure
In this case, the consequents must be further investigated to arrive at a complete formal rule. The inference engine can use the consequents derived in the knowledge representation to obtain a better definition of the problem’s cause. Knowledge and expert AI systems use this process to provide ad- vanced decision-making capabilities.
E X AM PLE 1 6 -1
A PLC-controlled box conveyor transports two sizes of boxes that are diverted to different palletizer operations according to their size. A solenoid activates the diverter that sorts the boxes. Write the rules that
a knowledge database could use to detect a possible cause for the solenoid’s malfunction.
S OLU T I ON
One of two factors can result in solenoid failure according to the situation presented: coil burnout or mechanical damage. The condi- tions and causes in Figure 16-3 describe these two possible factors that could lead to a fault.
Rule #1 Result
Condition
Cause
Burned-out coil Excessive temperature is
—Low line voltage causes
developed due to continuous
failure to pull plunger
high-current input
—High ambient temperature
—Mechanically blocked plunger
—Operations too rapid
Rule #2 Result
Excessive force exerted on
—Overvoltage
damage
—Reduced load Figure 16-3. Knowledge database rules for conveyor fault.
the plunger
Industrial Text & Video Company 1-800-752-8398
www.industrialtext.com
S ECTION Advanced PLC Artificial Intelligence C HAPTER 5 Topics and Networks
and PLC Systems 16
Parts
» An Industrial Text Company Publication Atlanta • Georgia • USA
» C HAPTER T HREE L OGI C C ON CEPT S
» 3 -3 P RINCIPLES OF B OOLEAN A LGEBRA AND L OGIC
» 3 -4 PLC C I RCU I T S AN D L OGI C C ON TACT S Y M BOLOGY
» C ONTACT S YMBOLS U SED IN PLC S
» L OADING C O N S I D E R AT I O N S
» M E M O RY C A PA C I T Y AND U T I L I Z AT I O N
» A P P L I C AT I O N M E M O RY
» D AT A T ABLE O R G A N I Z AT I O N
» 6 -2 I /O R ACK E NCLOSURES AND T ABLE M APPING
» I /O R ACK AND T ABLE M APPING E XAMPLE
» 6 -4 P L C I NSTRUCTIONS FOR D ISCRETE I NPUTS
» 6 -6 P L C I NSTRUCTIONS F OR D ISCRETE O UTPUTS
» 7 -3 A NALOG I NPUT D ATA R E P R E S E N TAT I O N
» 7 -4 A NALOG I NPUT D ATA H ANDLING
» 7 -6 O V E RV I E W OF A NALOG O UTPUT S IGNALS
» 7 -8 A NALOG O UTPUT D ATA R E P R E S E N TAT I O N
» 7 -9 A NALOG O UTPUT D ATA H ANDLING
» C HAPTER E IGHT S PECI AL F U N CT I ON I /O AN D S ERI AL C OM M U N I CAT I ON I N T ERFACI N G
» T HERMOCOUPLE I NPUT M ODULES
» E NCODER /C OUNTER I N T E R FA C E S
» S TEPPER M OTOR I N T E R FA C E S
» S ERVO M OTOR I N T E R FA C E S
» N ETWORK I N T E R FA C E M ODULES
» S ERIAL C O M M U N I C AT I O N
» I N T E R FA C E U SES AND A P P L I C AT I O N S
» 9 -3 L ADDER D IAGRAM F O R M AT
» 9 -5 L ADDER R E L AY P ROGRAMMING L ADDER S CAN E V A L U AT I O N
» P ROGRAMMING N O R M A L LY C LOSED I NPUTS
» 9 -1 0 A RITHMETIC I NSTRUCTIONS
» 9 -1 4 N ETWORK C O M M U N I C AT I O N I NSTRUCTIONS
» L ANGUAGES AND I NSTRUCTIONS
» F UNCTION B LOCK D IAGRAM (FBD)
» S EQUENTIAL F UNCTION C H A RT S (SFC)
» P ROGRAMMING L ANGUAGE N O TAT I O N
» P ROGRAMMING N O R M A L LY C LOSED T RANSITIONS
» D IVERGENCES AND C ONVERGENCES
» -1 C ONTROL T ASK D EFINITION
» C REAT I N G F LOWCH ART S AN D O U T PU T S EQU EN CES
» C ONFIGURING THE PLC S YSTEM
» S PECIAL I NPUT D EVICE P ROGRAMMING
» S IMPLE S TA R T /S TOP M OTOR C IRCUIT
» F O RWA R D /R EVERSE M OTOR I NTERLOCKING
» AC M OTOR D RIVE I N T E R FA C E
» L ARGE R E L AY S YSTEM M O D E R N I Z AT I O N
» A NALOG I NPUT C OMPARISON AND D ATA L INEARIZATION
» A NALOG P OSITION R EADING F ROM AN LV D T
» L INEAR I N T E R P O L AT I O N OF N ONLINEAR I NPUTS
» L ARGE B AT C H I N G C ONTROL A P P L I C AT I O N
» -7 S H O RT P ROGRAMMING E XAMPLES
» -1 B ASIC M EASUREMENT C ONCEPTS D ATA I N T E R P R E TAT I O N
» I NTERPRETING C OMBINED E RRORS
» B RIDGE C IRCUIT T ECHNIQUES
» R ESISTANCE T E M P E R AT U R E D ETECTORS ( RT D S )
» -1 P ROCESS C ONTROL B ASICS
» I N T E R P R E TAT I O N OF E RROR
» T RAN SFER F U N CT I ON S AN D T RAN SI EN T R ESPON SES
» D E R I V AT I V E L APLACE T RANSFORMS
» Out () s = ( )( ) In () s Hp () s
» S ECOND -O RDER L AG R ESPONSES
» D IRECT -A CTING C ONTROLLERS
» T WO -P OSITION D ISCRETE C ONTROLLERS
» T HREE -P OSITION D ISCRETE C ONTROLLERS
» -5 P R O P O RT I O N A L C ONTROLLERS (P M ODE )
» PV () s ( 1 + Hc Hp () s () s ) = SP Hc Hp () s () s () s
» CV () t = K I ∫ 0 Edt + CV ( t = 0 )
» CV ( t = 2 ) = K I 0 Edt + ∫ CV ( t = 1 )
» -7 P R O P O RT I O N A L -I NTEGRAL C ONTROLLERS (PI M ODE )
» -8 D E R I VAT I V E C ONTROLLERS (D M ODE ) S TANDARD D E R I V AT I V E C ONTROLLERS
» -9 P R O P O RT I O N A L -D E R I VAT I V E C ONTROLLERS (PD M ODE )
» -1 2 C ONTROLLER L OOP T UNING
» Z IEGLER –N ICHOLS O PEN -L OOP T UNING M ETHOD
» I TA E O PEN -L OOP T UNING M ETHOD
» S O F T WA R E T UNING M ETHODS
» R ULE -B ASED K NOWLEDGE R E P R E S E N T AT I O N
» S T AT I S T I C A L AND P ROBABILITY A N A LY S I S
» -1 I NTRODUCTION TO F UZZY L OGIC
» -2 H I S T O RY OF F UZZY L OGIC
» -3 F UZZY L OGIC O P E R AT I O N
» F U Z Z I F I C AT I O N C OMPONENTS
» F UZZY P ROCESSING C OMPONENTS
» D E F U Z Z I F I C AT I O N C OMPONENTS
» S YSTEM D ESCRIPTION AND O P E R AT I O N
» M EMBERSHIP F UNCTIONS AND R ULE C R E AT I O N
» IF A = PS AND B = NS THEN C = ZR IF A = PS AND B = NS THEN D = NS
» C HAPTER N INETEEN I /O B US N ET WORK S
» -4 D EVICE B US N ETWORKS B YTE -W IDE D EVICE B US N ETWORKS
» B IT -W IDE D EVICE B US N ETWORKS
» F IELDBUS P ROCESS B US N ETWORK
» P ROFIBUS P ROCESS B US N ETWORK
» I /O B US N ETWORK A DDRESSING
» P ANEL E NCLOSURES AND S YSTEM C OMPONENTS
» -3 N OISE , H E AT , AND V O LTA G E R EQUIREMENTS
» T ROUBLESHOOTING PLC I NPUTS
» -2 P L C S IZES AND S COPES OF A P P L I C AT I O N S
» I NPUT /O UTPUT C O N S I D E R AT I O N S
» C ONTROL S YSTEM O R G A N I Z AT I O N
» E Q U I VA L E N T L ADDER /L OGIC D IAGRAMS
Show more