based control and engineering systems by: natalia danilova advisor: prof. tatiana · pdf...

By:By:

Natalia Danilova

Natalia Danilova

Advisor:

Advisor:

Prof. Tatiana A. Gavrilova

Prof. Tatiana A. Gavrilova

SPbSPU

SPbSPUJASS 2008

JASS 2008

KNOWLEDGE

KNOWLEDGE-- BASED CONTROL

BASED CONTROL

AND ENGINEERING SYSTEMS

AND ENGINEERING SYSTEMS

Intr

oduc

tion

Con

cept

sA

ppro

ache

sC

ase-

stud

ies

Per

spec

tives

Con

clus

ion

N. Danilova, Knowledge-Based Control and Engineering Systems

2

Co

nte

nts

Kno

wle

dge-

base

dsy

stem

sar

eth

em

ostm

atur

ean

dw

idel

y-us

edco

mm

erci

alfie

ldof

artif

icia

lint

ellig

ence

. The

sp

ectr

um o

f app

licat

ions

of t

hese

sys

tem

s to

indu

stria

l and

co

mm

erci

al p

robl

ems

is s

o w

ide

as to

def

y ea

sy

char

acte

rizat

ion.

The

app

licat

ions

find

thei

r w

ay in

to m

ost

area

s of

kno

wle

dge

wor

k.K

now

ledg

e-ba

sed

syst

ems

achi

eve

high

leve

lsof

perf

orm

ance

inta

skar

eas

that

, for

hum

anbe

ings

, req

uire

year

sof

spec

iale

duca

tion

and

trai

ning

.


3

Intr

od

uct

ion

Diagnostics

Design

Decision support


4

Kn

ow

led

ge-

Bas

ed

Sys

tem

s (M

ind

Map

)

Diagnostics

Design

Decision support

Kn

ow

led

ge

and

Dat

a


5

DATA

DATA

DATA

DATA

Inst

ance

s an

d fa

cts

char

acte

rizin

g ob

ject

’s, p

roce

sses

and

thei

r

prop

ertie

s

KNOWLEDGE

Rul

es,

conc

aten

atin

g th

e da

ta

and

obta

ined

from

exp

erie

nce

.

Def

init

ion

A k

now

ledg

ekn

owle

dge

base

dba

sed

syst

emsy

stem

, als

okn

own

asan

exp

ert

expe

rtsy

stem

syst

em, i

sa

com

pute

r pr

ogra

m th

atco

ntai

nsth

ekn

owle

dge

and

anal

ytic

alsk

ills

ofon

eor

mor

ehu

man

expe

rts,

rel

ated

toa

spec

ific

subj

ect.

A s

oftw

are

that

per

form

s a

task

that

wou

ld o

ther

wis

e be

per

form

ed b

y a

hum

an e

xper

t


6

His

tory

The

Log

ical

P

erio

d

1960

-197

6

Axi

omat

ic a

ppro

ach

of

solv

ing

prob

lem

s

Heu

ristic

app

roac

h

1965

: LIS

P

(firs

t sym

bol

proc

essi

ng la

ngua

ge)

1973

: PR

OLO

G

(axi

om-b

ased

la

ngua

ge)

Kno

wle

dge

Bas

ed

Per

iod

1976

-199

0

Con

clus

ion

is m

ade

from

vas

t am

ount

of

prev

ious

kno

wle

dge

colle

cted

DE

ND

RA

L

MY

CIN

Indu

stria

l A

I Sys

tem

s

1990

-now

aday

s

Exp

erts

yste

ms

are

appl

ied

toa

wid

eva

riety

ofar

eas


6

Th

e M

ain

Dif

fere

nce

Fro

m O

ther

Pro

gra

ms

•E

S m

odel

nott

heph

ysic

sof

the

dom

ain

butt

hew

ayof

prob

lem

solv

ing

byhu

man

expe

rtor

the

way

thin

king

and

know

ledg

epr

oces

sing

inth

isdo

mai

n.

•E

S m

ake

allc

oncl

usio

nsus

ing

Kno

wle

dge

that

ispu

tby

expe

rtan

dkn

owle

dge

engi

neer

.

•M

athe

mat

ics

isno

tess

entia

lin

ES

, heu

ristic

san

dfu

zzy

met

hods

are

mor

esu

bsta

ntia

l.


8

Exp

ert

Sys

tem

S

tru

ctu

re


9

Kn

ow

led

ge

eng

inee

rEn

d u

ser

INFERENCE

ENGINE

USER

INTERFACE

KNOWLEDGE BASE

E EEE X XXX P PPP E EEE R RRR T TTT S SSS Y YYY S SSS T TTT E EEE M MMM

Sol

utio

ns

Sol

utio

ns

Rul

es

Fac

ts a

bout

prob

lem

Fac

ts a

bout

prob

lem

Pra

ctic

al d

ata

(app

lied

know

ledg

e)H

ard

data

(sci

entif

ic k

now

ledg

e)

Pub

lishe

d da

taF

ield

stu

dies

Exp

ert

Mai

n B

lock

s

KNOWLEDGE BASE

cons

ists

ofse

nten

ces

whi

chde

fine

know

ledg

ew

ithth

eus

eof

supe

r-hi

gh-le

vell

angu

ages

whi

char

eca

lled

know

ledg

ere

pres

enta

tion

lang

uage

s. It

isth

eke

rnel

ofth

eex

pert

syst

em.

INFERENCE ENGINE

isa

prog

ram

whi

chsi

mul

ates

the

proc

ess

ofex

pertre

ason

ing

orde

cisi

onm

akin

g.

The

func

tion

ofth

eUSER INTERFACE

isto

pres

entq

uest

ions

and

info

rmat

ion

toth

eus

eran

dsu

pply

the

user

'sre

spon

ses

to

the

infe

renc

een

gine

. N. Danilova, Knowledge-Based Control and Engineering Systems

10


11

Cla

ssif

icat

ion

By relation

By relation

By relation

By relation

to real time

to real time

to real time

to real time

Sta

ticS

tatic

Qua

siQ

uasi

-- dyn

amic

dyna

mic

Dyn

amic

Dyn

amic

By problem

By problem

By problem

By problem

Dat

a D

ata

Inte

rpre

tatio

nIn

terp

reta

tion

DIAGNOSTICS

DIAGNOSTICS

DIAGNOSTICS

DIAGNOSTICS

CONTROL

CONTROL

CONTROL

CONTROL

For

ecas

tF

orec

ast

Pla

nnin

gP

lann

ing

Inst

ruct

ion

Inst

ruct

ion

DESIGN

DESIGN

DESIGN

DESIGN

DECISION SUPPORT

DECISION SUPPORT

DECISION SUPPORT

DECISION SUPPORT

By computer

By computer

By computer

By computer

type

type

type

type

On

supe

rcom

pute

rO

n su

perc

ompu

ter

On

mai

nfra

mes

On

mai

nfra

mes

On

sym

bolic

O

n sy

mbo

lic

proc

esso

rspr

oces

sors

On

wor

ksta

tions

On

wor

ksta

tions

On

pers

onal

O

n pe

rson

al

com

pute

rco

mpu

ter

By

By

By

By

integration

integration

integration

integration

degree

degree

degree

degree

Sta

ndS

tand

-- alo

neal

one

Hyb

ridH

ybrid

(( int

egra

ted

inte

grat

ed))

Exp

ert

Sys

tem

Lif

eC

ycle


12

Kno

wle

dge

Acq

uisi

tion

Kno

wle

dge

Rep

rese

ntat

ion

Pro

toty

pe

syst

em

Com

mer

cial

syst

em

Pro

blem

D

efin

ition

Ad

van

tag

es a

nd

Dis

adva

nta

ges

•P

rovi

des

cons

iste

ntan

swer

sfo

rre

petit

ive

deci

sion

s,

proc

esse

san

dta

sks

•H

olds

and

mai

ntai

nssi

gnifi

cant

leve

lsof

info

rmat

ion

•E

ncou

rage

sor

gani

zatio

nsto

clar

ifyth

elo

gic

ofth

eir

deci

sion

-mak

ing

•N

ever

"for

gets

" to

ask

a qu

estio

n, a

sa

hum

anm

ight

•La

cks

com

mon

sens

ene

eded

inso

me

deci

sion

mak

ing

•C

anno

tmak

ecr

eativ

ere

spon

ses

ashu

man

expe

rtw

ould

inun

usua

lci

rcum

stan

ces

•E

rror

sm

ayoc

cur

inth

ekn

owle

dge

base

, and

lead

tow

rong

deci

sion

s•

Can

nota

dapt

toch

angi

ngen

viro

nmen

ts, u

nles

skn

owle

dge

base

isch

ange

d


13

Fo

rwar

d c

hai

nin

g(D

ata

Dri

ven

rea

son

ing

)

The

syst

emke

eps

trac

kof

the

curr

ents

tate

ofpr

oble

mso

lutio

nan

dlo

oks

for

rule

sw

hich

will

mov

eth

atst

ate

clos

erto

a fin

also

lutio

n.

The

syst

emm

ustb

ein

itial

lypo

pula

ted

with

data

,in

cont

rast

toth

ego

aldr

iven

syst

emw

hich

gath

ers

data

asit

need

sit.

DA

TA

Rul

e 1

Rul

e 2

Rul

e N

Rul

e 3

Pos

sibi

lity

1

Pos

sibi

lity

2

Pos

sibi

lity

K

A=

1If

A=

1 &

B=

2 th

en C

=3;

If

C=

3 th

en D

=4

D=

4B

=2


14

KNOWLEDGE

KNOWLEDGE

KNOWLEDGE

KNOWLEDGE

PROBLEM

PROBLEM

PROBLEM

PROBLEM

Bac

kwar

d C

hai

nin

g(G

oal

-Dri

ven

Rea

son

ing

)

An

effic

ient

way

toso

lve

"str

uctu

red

sele

ctio

n" p

robl

ems.

Rul

e 1

Rul

e 2

Rul

e N

Rul

e 3

Sub

-pro

blem

3

Sub

-pro

blem

4

Sub

-pro

blem

M

Sub

-pro

blem

2

Sub

-pro

blem

1P

ossi

bilit

y 1

Pos

sibi

lity

2

Pos

sibi

lity

K

A=

1If

A=

1 &

B=

2 th

en C

=3;

If

C=

3 th

en D

=4

D=

4B

=2


15

Cas

e-S

tud

ies

Cas

e 1:

Bla

st F

urna

ce C

ontr

ol

•T

he c

ompa

ny: S

teel

Com

pany

's F

ukuy

ama

Wor

ks, J

apan

•

The

pro

blem

: Bec

ause

the

blas

t fur

nace

feed

s al

l oth

er

proc

esse

s in

the

stee

l mill

, any

inst

abili

ty in

th

e op

erat

ion

of th

e fu

rnac

e is

com

poun

ded

by th

e im

pact

on

othe

r pr

oces

ses

furt

her

dow

n th

e pr

oduc

tion

line.

•

The

pur

pose

: the

pre

dict

ion

of a

bnor

mal

con

ditio

ns w

ithin

the

blas

t fur

nace

(to

min

imiz

e th

e un

cert

aint

yin

the

oper

atin

g te

mpe

ratu

re)


16

Cas

e 1:

B

last

Fu

rnac

e C

on

tro

l•

Sub

-pro

blem

s:–

char

acte

rizin

g th

e cu

rren

t sta

te o

f the

furn

ace

and

proj

ectin

g th

e co

nditi

ons

occu

r se

vera

l hou

rs;

–tr

aini

ng a

ski

lled

blas

t fur

nace

ope

rato

r ta

kes

man

y ye

ars;

–th

e co

mpl

exity

of m

odel

ing

a bl

ast f

urna

ce;

–th

ere

are

no s

ymm

etrie

s to

sim

plify

the

geom

etric

m

odel

ing;

–th

e th

erm

al s

tate

of t

he fu

rnac

e ca

nnot

be

mea

sure

d di

rect

ly, b

ut m

ust b

e in

ferr

ed fr

om v

ario

us s

enso

r m

easu

rem

ents

;


17

The

sm

alle

r th

e un

cert

aint

y, th

e lo

wer

the

over

all t

empe

ratu

re

need

ed to

pro

duce

the

pig

iron,

res

ultin

g in

ver

y la

rge

fuel

sav

ings

.

Fue

lCos

tSav

ings

:


18

Cas

e 1:

B

last

Fu

rnac

e C

on

tro

l

The

feat

ures

of t

he e

xper

t sys

tem

:

•m

odel

s th

e cu

rren

t sta

te;

•pr

edic

ts fu

ture

tren

ds w

ith s

uffic

ient

accu

racy

to m

ake

cont

rol d

ecis

ions

;•

mak

es th

e co

ntro

l dec

isio

ns;

•de

cisi

ons

can

be im

plem

ente

d au

tom

atic

ally

or

man

ually

Cas

e 1:

Bla

st F

urn

ace

Co

ntr

ol


19

BF

sen

sors


20

Bla

stF

urna

ceE

xper

tSys

tem

:

Hot

sto

ve

Wat

er in

ject

ion

Ste

am Air

Dis

trib

uted

dig

ital

cont

rolle

rHot

sto

veco

ntro

ller

Gas

cle

anin

gco

ntro

ller

Bla

se

tem

pera

ture

Bla

se m

oist

ure

(Pro

cess

com

pute

r)(A

I pro

cess

or) In

fere

nce

engi

ne Kno

wle

dge

base

Con

clus

ion

Req

uest

Dat

a pr

oces

sing

for

reas

onin

g

Pro

cess

data

bas

e

Fur

nace

co

ntro

l

Sen

sor

data

Gat

herin

g

CR

TC

RT

IF…

TH

EN

…

Use

r t/

F

Mol

ten

iron,

sl

ag

Rac

eway

Coh

esiv

e zo

ne

Sys

tem

Com

pone

nts:

•a

proc

ess

com

pute

rga

ther

s in

put d

ata

from

var

ious

se

nsor

s in

the

furn

ace,

mai

ntai

ns a

pro

cess

dat

abas

e an

d ge

nera

tes

furn

ace

cont

rol i

nfor

mat

ion;

•th

e A

I pro

cess

orpr

ovid

es th

e kn

owle

dge

and

reas

onin

g fo

r as

sess

ing

and

inte

rpre

ting

the

sens

or d

ata,

hy

poth

esiz

ing

the

inte

rnal

sta

te o

f the

furn

ace,

and

de

term

inin

g ap

prop

riate

con

trol

act

ions

;

•a

dist

ribut

ed d

igita

l con

trol

ler

uses

the

furn

ace

cont

rol

data

from

the

proc

ess

com

pute

r to

con

trol

the

actu

al

blas

t fur

nace

.


21

Cas

e 1:

Bla

st F

urn

ace

Co

ntr

ol

Res

ult:

•C

ompa

ny a

nnua

l sav

ings

of $

6 m

illio

n•

Red

uctio

n in

sta

ff of

4 p

eopl

e

•Im

prov

emen

t in

the

qual

ity o

f the

furn

ace

outp

ut

Det

ails

:

•T

he s

yste

m is

impl

emen

ted

in L

ISP

with

FO

RT

RA

N u

sed

for

data

pr

epro

cess

ing

•T

he k

now

ledg

e in

the

AI p

roce

ssor

is c

onta

ined

in 4

00 r

ules

, 35

0 fr

ames

, and

200

LIS

P p

roce

dure

s

•F

uzzy

theo

ry is

em

ploy

ed in

its

infe

renc

e en

gine

•T

he s

yste

m h

as a

cyc

le ti

me

of 2

0 m

inut

es, c

ompa

red

to th

e fu

rnac

e tim

e co

nsta

nt o

f six

to e

ight

hou

rs.

Cas

e 1:

Bla

st F

urn

ace

Co

ntr

ol


22

Cas

e 2:

Mo

ther

bo

ard

Exp

ert

Sys

tem

•T

he p

robl

em: n

eed

to c

all t

o th

e se

rvic

e ce

nter

to a

sk fo

r th

e so

lutio

n fr

om th

e te

chni

cian

and

be

char

ged

for

that

; har

d un

ders

tand

ing

of th

ete

rms

writ

ten

in m

anua

l

•T

he p

urpo

se: t

o re

cogn

ize,

man

age

and

fix th

e m

othe

rboa

rd’s

pro

blem

, and

pro

vide

use

rsw

ith a

ppro

pria

te s

olut

ion

base

on

the

accu

rate

dia

gnos

is


23

The

feat

ures

of t

he e

xper

t sys

tem

:

•fig

ures

out

the

mai

n pr

oble

ms

of th

e M

SI’s

mot

herb

oard

;

•gi

ves

solu

tion

rega

rdin

g to

the

accu

rate

di

agno

sisCas

e 2:

Mo

ther

bo

ard

Exp

ert

Sys

tem


24

Met

hodo

logy

:•

Pha

se 1

: Pro

blem

Ass

essm

ent

–ga

ther

ing

all t

he in

form

atio

n–

iden

tifyi

ng th

e go

als

and

requ

irem

ents

•P

hase

2: K

now

ledg

e A

cqui

sitio

n an

d A

naly

sis

–co

llect

ion

of k

now

ledg

e (in

terv

iew

ing

the

MS

I mot

herb

oard

str

uctu

re e

xper

ts, c

olle

ctio

n da

ta fr

om th

e us

erm

anua

l boo

ks a

nd th

e da

ta fr

om th

e M

SI’s

web

site

).

•P

hase

3: D

esig

n an

d Im

plem

enta

tion

–ru

les,

sys

tem

pro

gram

min

g pa

rt a

nd s

yste

m in

terf

ace

Cas

e 2:

Mo

ther

bo

ard

Exp

ert

Sys

tem


25

•P

hase

4: T

estin

g–

all t

he k

now

ledg

e an

d ru

les

in th

e sy

stem

are

tota

lly a

ccur

ate

with

the

know

ledg

e th

at c

olle

cted

from

the

expe

rtis

e–

the

abili

ty o

f Mo

bE

Son

sol

ving

the

user

’s p

robl

ems

–us

ers

are

com

fort

able

with

the

desi

gn–

the

syst

em r

each

es th

e go

als

(pro

vide

s so

lutio

n an

d ad

vice

to

the

user

s so

that

they

can

han

dle

thei

r ow

n co

mpu

ter

prob

lem

s)

Mo

bE

Sac

tual

ly is

stil

l und

er d

evel

opm

ent

stat

us, a

nd it

stil

l nee

d tim

e an

d su

ppor

t fr

om th

e ex

pert

to r

each

it g

oals

.

Cas

e 2:

Mo

ther

bo

ard

Exp

ert

Sys

tem


26

•T

echn

olog

y w

ill c

lear

ly b

ecom

e m

ore

help

ful i

n de

alin

gw

ith in

form

atio

n ov

erlo

ad.

•T

he c

urre

nt c

apab

ility

of m

achi

ne in

telli

genc

e is

suc

h th

athu

man

kno

wle

dge

will

con

tinue

to b

e a

valu

able

res

ourc

e fo

rth

e fo

rese

eabl

e fu

ture

, and

tech

nolo

gy to

hel

p to

leve

rage

it w

ill b

e in

crea

sing

ly v

alua

ble

and

capa

ble.

•H

owev

er, i

n m

any

case

s ex

pert

s ar

e be

ing

aske

d to

surr

ende

r th

eir

know

ledg

e an

d ex

perie

nce—

the

very

trai

ts

that

mak

e th

em v

alua

ble

as in

divi

dual

s.


27

Per

spec

tive

s

Co

ncl

usi

on

Tha

t was

the

brie

f ove

rvie

w o

fkno

wle

dge-

base

d sy

stem

s fo

r co

ntro

l and

en

gine

erin

g. T

wo

diffe

rent

rea

l exp

ert

syst

ems

wer

e co

nsid

ered

–B

last

Fur

nace

C

ontr

ol s

yste

m a

nd M

othe

rboa

rd E

xper

t sy

stem

. Per

spec

tives

of k

now

ledg

e-ba

sed

syst

ems

deve

lopm

ent w

ere

conc

lude

d.


28

Ref

eren

ces

•A

mzi

inc.

Bui

ldin

gE

xper

tSys

tem

sin

Pro

log

: w

ww

.am

zi.c

om/E

xper

tSys

tem

sInP

rolo

g•

Cla

rke

R. K

now

ledg

e-B

ased

Exp

ertS

yste

ms

: w

ww

.anu

.edu

.au/

peop

le/R

oger

.Cla

rke/

SO

S/K

BT

.htm

l•

Day

J. K

now

ledg

e-ba

sed

Eng

inee

ring

–A

utom

atin

gfo

rP

rofit

abili

tyin

Pro

duct

Des

ign

: ww

w.d

esig

nnew

s.co

m•

Fei

genb

aum

E.,

Frie

dlan

dP

. Kno

wle

dge-

Bas

ed S

yste

ms

in J

apan

-w

ww

.wte

c.or

g/lo

yola

/kb/

c2_s

3.ht

m•

Gav

rilov

a T

. Cou

rse

of L

ectu

res

abou

t Kno

wle

dge

Eng

inee

ring

, -S

aint

-P

eter

sbur

g S

tate

Pol

ytec

hnic

alU

nive

rsity

, 200

7•

Nor

man

A. C

ours

e of

Lec

ture

s ab

out E

xper

t Sys

tem

s, -

Uni

vers

ity o

f Tex

as

at A

ustin

:ww

w.e

co.u

texa

s.ed

u/fa

culty

/Nor

man

/long

.ext

ra/In

fo.S

98/E

xp•

Sie

wW

ai.M

obE

SE

xper

tSys

tem

: w

ww

.gen

erat

ion5

.org

/con

tent

/200

5/M

obE

S.a

sp•

Tay

lor

J., S

trin

ger

P. A

nau

tosy

nthe

sizi

ngno

n-lin

ear

cont

rols

yste

mus

ing

a ru

le-b

ased

expe

rtsy

stem

:Int

erna

tiona

l Jou

rnal

of A

dapt

ive

Con

trol

and

Sig

nal

Pro

cess

ing,

5 M

ar20

07,V

olum

e 5,

Issu

e 1

, Pag

es21

–39

•W

ikip

edia

: ww

w.w

ikip

edia

.org


29


30

THANK YOU!

THANK YOU!

THANK YOU!

THANK YOU!

based control and engineering systems by: natalia danilova advisor: prof. tatiana · pdf...

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