iaea common cause failure (ccf) analysis and generic ccf ... · ng o f componen t s t a t es, d ue...
TRANSCRIPT
Inte
rnat
iona
l Ato
mic
Ene
rgy
Age
ncy
Inte
rnat
iona
l Ato
mic
Ene
rgy
Age
ncy
Com
mon
Cau
se F
ailu
re (
CC
F) A
naly
sis
and
Gen
eric
CC
F D
ata
~U
S E
xper
ienc
e
IAE
A T
echn
ical
Rev
iew
Mee
ting
Nov
embe
r 06-
08, 2
013
See
Men
g W
ong,
PhD
U.S
. Nuc
lear
Reg
ulat
ory
Com
mis
sion
See
-Men
gW
ong@
nrc
gov
11
See
Men
g.W
ong@
nrc.
gov
Pres
enta
tion
Out
line
Pres
enta
tion
Out
line
O
bjec
tives
P
rovi
de a
n ov
ervi
ew o
f Com
mon
Cau
se F
ailu
re (C
CF)
m
odel
ing
D
iscu
ss th
e ge
nera
l app
roac
h of
CC
F an
alys
is in
risk
g
yas
sess
men
t
C
CF
Mod
els
CC
F M
odel
s
Bas
ic P
aram
eter
Mod
el
Bet
a-Fa
ctor
Mod
el
Mlti
lG
kL
ttM
dl
M
ultip
le G
reek
Let
ter M
odel
A
lpha
-Fac
tor M
odel
A
naly
sis
Proc
ess
22
Com
mon
Cau
seFa
ilure
s(C
CFs
)C
omm
on C
ause
Fai
lure
s (C
CFs
)
Su
bset
ofD
epen
dent
Failu
res
inw
hich
two
orm
ore
Subs
et o
f Dep
ende
nt F
ailu
res
in w
hich
two
or m
ore
com
pone
nt fa
ult s
tate
s ex
ist a
t the
sam
e tim
e, o
r w
ithin
a s
hort
tim
e in
terv
al, a
s a
resu
lt of
a s
hare
d ca
use
caus
e
Th
e sh
ared
cau
se is
not
ano
ther
com
pone
nt s
tate
b
hdi
ft
tt
dbe
caus
e su
ch c
asca
ding
of c
ompo
nent
sta
tes,
due
to
func
tiona
l cou
plin
gs, a
re a
lread
y us
ually
mod
eled
ex
plic
itly
in s
yste
m m
odel
s
R
esid
ual d
epen
dent
failu
res
who
se ro
ot c
ause
s ar
e no
texp
licitl
ym
odel
edin
the
PSA
not e
xplic
itly
mod
eled
in th
e PS
A
33
Why
isC
CF
Mod
elin
gIm
port
ant?
Why
is C
CF
Mod
elin
g Im
port
ant?
U
.S.c
omm
erci
alnu
clea
rpow
erpl
ants
(NPP
s)U
.S. c
omm
erci
al n
ucle
ar p
ower
pla
nts
(NPP
s)
are
desi
gned
with
saf
ety
as fo
rem
ost p
riorit
y
Red
unda
ncy
D
iver
sity
D
efen
se in
dep
th
Saf
ety
mar
gins
N
PPde
sign
sar
eef
fect
ivel
ysi
ngle
failu
re
NPP
des
igns
are
effe
ctiv
ely
sing
le fa
ilure
“p
roof
”
O
nly
com
bina
tions
of f
ailu
res
can
serio
usly
ch
alle
nge
reac
tor i
nteg
rity
4
Exam
ples
ofC
CF
Exam
ples
of C
CF
H
uman
Inte
ract
ions
M
aint
enan
ce te
chni
cian
inco
rrec
tly s
ets
setp
oint
son
mul
tiple
co
mpo
nent
s
Inco
rrec
t or i
ncor
rect
ly a
pplie
d lu
bric
ant
Ph
ysic
al o
r env
ironm
enta
l con
ditio
ns
Bio
-foul
ing
(e.g
., cl
ams,
mus
sels
, fis
h, k
elp,
etc
.)g
(g
p)
D
esig
n or
man
ufac
turin
g de
fect
C
onta
min
atio
n in
lubr
ican
t or f
uel
N
ot re
pres
ente
d ex
plic
itly,
onl
y pa
ram
etric
ally
5
Dep
ende
nt F
ailu
res
p
Com
bina
tions
of i
ndep
ende
nt fa
ilure
s ar
e ex
trem
ely
rare
even
tsex
trem
ely
rare
eve
nts
D
epen
dent
failu
res
pose
maj
or c
halle
nge
to
safe
ty
Sha
red
equi
pmen
t and
sup
port
syst
em
depe
nden
cies
depe
nden
cies
•E
xplic
itly
mod
eled
in P
SA
logi
c
Fa
ilure
s of
mul
tiple
com
pone
nts
from
a c
omm
on (o
r sh
ared
) cau
se•
Cau
se n
ot e
xplic
itly
mod
eled
py
•Tr
eate
d pa
ram
etric
ally
–C
CF
mod
els
6
Def
initi
onof
Dep
ende
ncy
Def
initi
on o
f Dep
ende
ncy
Ev
ents
Aan
dB
are
said
tobe
depe
nden
t
Even
ts A
and
B a
re s
aid
to b
e de
pend
ent
even
ts if
P(A
*B) =
P(A
|B) *
P(B
)=
P(B
|A) *
P(A
)≠
P(A
) * P
(B)
Ty
pica
lly if
eve
nts
are
depe
nden
tP
(A*B
) > P
(A) *
P(B
)(
)(
)(
)
Th
is is
why
dep
ende
nt e
vent
s ar
e a
safe
ty c
once
rn
7
Type
s of
Dep
ende
nt E
vent
s B
ased
on
Thei
rI
tPS
AM
dl
Impa
ct o
n a
PSA
Mod
el
8
CC
FM
odel
sC
CF
Mod
els
B
asic
Par
amet
er
Bet
aFa
ctor
Bet
a Fa
ctor
M
ultip
le G
reek
Let
ter (
MG
L)
Alp
haFa
ctor
A
lpha
Fac
tor
9
(1)B
asic
Para
met
erM
odel
(1) B
asic
Par
amet
er M
odel
1010
(1)B
asic
Para
met
erM
odel
(1) B
asic
Par
amet
er M
odel
QQQQ
QQAA
QQ33
QQ11 Q2Q2
1111
(1)B
asic
Para
met
erM
odel
(1) B
asic
Par
amet
er M
odel
M
otiv
atio
n fo
r Par
amet
ric M
odel
s
D
ata
need
ed to
est
imat
e Q
kin
bas
ic
para
met
erm
odel
are
notg
ener
ally
avai
labl
epa
ram
eter
mod
el a
re n
ot g
ener
ally
ava
ilabl
e−
Gen
eric
failu
re p
roba
bilit
ies/
rate
s fo
r com
pone
nts
(i.e.
, Qt)
−C
ompi
latio
nsof
depe
nden
tfai
lure
s(w
ithou
t−
Com
pila
tions
of d
epen
dent
failu
res
(with
out
dem
and
data
)
A
ltti
dl
ltt
if
tit
A
ltern
ativ
e m
odel
s us
e la
tter i
nfor
mat
ion
to
deve
lop
rela
tive
fract
ions
of d
epen
dent
failu
re
even
ts
1212
(2) β
-Fac
tor M
odel
O
rigin
ally
dev
elop
ed fo
r 2-c
ompo
nent
sys
tem
s; la
ter e
xten
ded
to
hdl
lt
hand
le la
rger
sys
tem
s
B
ased
on
notio
n th
at c
ompo
nent
failu
res
can
be d
ivid
ed in
to tw
o gr
oups
grou
ps
Inde
pend
ent f
ailu
res
D
epen
dent
failu
re o
f all
com
pone
nts
A
lloca
tion
mod
el:
Qt=
Q1+
Qm=
(1 –β)
Qt+
βQ
t
Inde
pend
ent c
ontri
butio
n D
epen
dent
con
tribu
tion
Ther
efor
e: β≡
Qm/(Q
1+Q
m)
13
(2)β
-Fac
torM
odel
(2) β
Fact
or M
odel
β-
Fact
or E
stim
atio
n ~
Exa
mpl
e p
C
onsi
der a
sys
tem
with
two
com
pone
nts:
A a
nd B
C
ompo
nent
A h
as fa
iled
3 tim
es in
50,
000
hour
s of
ser
vice
; ou
t of t
hose
3 fa
ilure
eve
nts,
1 e
vent
was
a c
omm
on c
ause
fa
ilure
(invo
lvin
gco
mpo
nent
B)
failu
re (i
nvol
ving
com
pone
nt B
)
Com
pone
nt B
als
o ha
s 50
,000
hou
rs o
f ser
vice
, and
it h
as
faile
d 2
times
(inc
ludi
ng th
e jo
int f
ailu
re e
vent
with
A)
P
oint
est
imat
es fo
r λtan
d β
are
calc
ulat
ed a
s fo
llow
s:
λ t=
5 fa
ilure
s/ 1
00,0
00 h
r= 5
.0 x
10-
5 /hr
β=
2/(3
+2) =
0.4
λ C
CF=
λt* β=
5.0
x 1
0-5 /h
r* 0
.4
λ C
CF=
2.0
x 1
0-5 /h
r
In
the
abse
nce
ofpl
ants
peci
ficda
taba
seco
mpo
nent
failu
rera
te
In th
e ab
senc
e of
pla
nt-s
peci
fic d
ata,
bas
e co
mpo
nent
failu
re ra
te
(λt)
is o
btai
ned
from
gen
eric
failu
re ra
tes
14
(3)M
ultip
leG
reek
Lette
r(M
GL)
Mod
el(3
) Mul
tiple
Gre
ek L
ette
r (M
GL)
Mod
el
•β-
fact
or e
xten
sion
to tr
eat m
ultip
le le
vels
of C
CF
βp
•D
efin
ition
s:D
efin
ition
s:β
= co
nditi
onal
pro
babi
lity
that
the
caus
e of
a s
peci
fic c
ompo
nent
fa
ilure
will
be s
hare
d by
one
or m
ore
addi
tiona
l com
pone
nts
ϒ=co
nditi
onal
prob
abilit
yth
atco
mm
onca
use
failu
reof
asp
ecifi
cϒ= co
nditi
onal
pro
babi
lity
that
com
mon
cau
se fa
ilure
of a
spe
cific
co
mpo
nent
that
has
faile
d tw
o co
mpo
nent
s w
ill b
e sh
ared
by
one
or
mor
e ad
ditio
nal c
ompo
nent
s∆
=co
nditi
onal
prob
abili
tyth
atco
mm
onca
use
failu
reof
asp
ecifi
c∆
= co
nditi
onal
pro
babi
lity
that
com
mon
cau
se fa
ilure
of a
spe
cific
co
mpo
nent
that
has
faile
d th
ree
com
pone
nts
will
be
shar
ed b
y on
e or
mor
e ad
ditio
nal c
ompo
nent
s
15
(3)M
ultip
leG
reek
Lette
r(M
GL)
Mod
el(3
) Mul
tiple
Gre
ek L
ette
r (M
GL)
Mod
el
DDBB
CC
1616AA
(3)M
ultip
leG
reek
Lette
r(M
GL)
Mod
el(3
) Mul
tiple
Gre
ek L
ette
r (M
GL)
Mod
el
1717
(4)A
lpha
Fact
orM
odel
(4) A
lpha
Fac
tor M
odel
S
impl
eex
pres
sion
sfo
rexa
ctdi
strib
utio
nsof
Sim
ple
expr
essi
ons
for e
xact
dis
tribu
tions
of
MG
L pa
ram
eter
s (a
ccou
ntin
g fo
r unc
erta
intie
s)
are
not a
lway
s ob
tain
able
A
ppro
xim
ate
met
hods
lead
ing
to p
oint
es
timat
ors
prov
ided
earli
erun
dere
stim
ate
estim
ator
s pr
ovid
ed e
arlie
r und
eres
timat
e un
certa
inty
ά-
fact
or m
odel
dev
elop
ed to
add
ress
this
issu
e
1818
(4)A
lpha
Fact
orM
odel
(4) A
lpha
Fac
tor M
odel
1919
(4)A
lpha
Fact
orM
odel
(4) A
lpha
Fac
tor M
odel
2020
Ana
lysi
sPr
oces
sA
naly
sis
Proc
ess
G
ener
alS
teps
Gen
eral
Ste
ps
Sta
rting
with
sys
tem
logi
c m
odel
, ide
ntify
co
mm
onca
use
com
pone
ntgr
oups
com
mon
cau
se c
ompo
nent
gro
ups
D
evel
op C
CF
mod
el
Gat
hera
ndan
alyz
eda
ta
Gat
her a
nd a
naly
ze d
ata
Q
uant
ify C
CF
mod
el p
aram
eter
s
Qua
ntify
CC
Fba
sic
even
ts
Qua
ntify
CC
F ba
sic
even
ts
2121
Ana
lysi
sPr
oces
sA
naly
sis
Proc
ess
Id
entif
y “C
omm
on C
ause
Com
pone
nt G
roup
s”
D
efin
ition
:Agr
oup
ofco
mpo
nent
sth
atha
sa
sign
ifica
ntlik
elih
ood
ofex
perie
ncin
ga
D
efin
ition
: A g
roup
of c
ompo
nent
s th
at h
as a
sig
nific
ant l
ikel
ihoo
d of
exp
erie
ncin
g a
com
mon
cau
se fa
ilure
eve
nt
C
onsi
der s
imila
rity
of:
•C
ompo
nent
type
pyp
•M
anuf
actu
rer
•M
ode
of o
pera
tion/
mod
e of
failu
re•
Envi
ronm
ent
•Lo
catio
n•
Mis
sion
•M
issi
on•
Test
and
Mai
nten
ance
Pro
cedu
res
“C
omm
on C
ause
Com
pone
nt G
roup
s” S
cree
ning
Pro
cess
D
iver
sity
(eg
inop
erat
ion
mis
sion
s)is
apo
ssib
lere
ason
fors
cree
ning
D
iver
sity
(e.g
., in
ope
ratio
n, m
issi
ons)
is a
pos
sibl
e re
ason
for s
cree
ning
ou
t •N
ote:
div
erse
com
pone
nts
can
have
com
mon
pie
ce p
arts
(e.g
., co
mm
on
pum
ps, d
iffer
ent d
river
s)
2222
Ana
lysi
sPr
oces
sA
naly
sis
Proc
ess
Iffi
iFl
Dev
elop
men
t of C
CF
Mod
elIn
suffi
cien
t Flo
w
From
2/3
EC
I Tr
ains
pE
xplic
it re
pres
enta
tion
exam
ple
Spe
cific
com
bina
tions
of
com
pone
nts
are
expl
icitl
y
Inde
pend
ent
Har
dwar
e Fa
ilure
O
f Pum
p Tr
ains
Com
mon
-Cau
se
Failu
re O
f Pu
mps
show
n on
faul
t tre
e
Com
mon
-Cau
se
Failu
re O
f Pu
mps
A a
nd B
Com
mon
-Cau
se
Failu
re O
f Pu
mps
B a
nd C
Com
mon
-Cau
se
Failu
re O
f Pu
mps
A a
nd C
Com
mon
-Cau
se
Failu
re O
f Pu
mps
A, B
, and
C
2323
Ana
lysi
sPr
oces
sA
naly
sis
Proc
ess
Im
plic
itm
odel
ing
exam
ple
(3tra
ins)
Impl
icit
mod
elin
g ex
ampl
e (3
trai
ns)
P
{top
even
t due
to C
CF}
= 3
Q2
+ Q
3
P
roba
bilit
ies
ofdi
ffere
ntco
mbi
natio
nsar
e“r
olle
d-up
”
Pro
babi
litie
s of
diff
eren
t com
bina
tions
are
rol
led
up
into
the
CC
F te
rm.
Insu
ffic
ient
Flow
Insu
ffic
ient
Flo
w
From
2/3
EC
I Tr
ains
Inde
pend
ent
Har
dwar
e Fa
ilure
O
f Pum
p Tr
ains
Syst
em F
ails
du
e to
CC
F
2424
Dat
aA
naly
sis
Proc
ess
Dat
a A
naly
sis
Proc
ess
D
ata
Sou
rces
D
ata
Sou
rces
G
ener
ic ra
w d
ata
com
pila
tions
(e.g
., Li
cens
ee E
vent
R
t(L
ER
)LE
Ri
NP
E)
Rep
orts
(LE
Rs)
, LE
R s
umm
arie
s, N
PE
)
Pla
nt-s
peci
fic ra
w d
ata
reco
rds
(e.g
., te
st a
nd
mai
nten
ance
reco
rds
wor
kor
ders
oper
ator
logs
)m
aint
enan
ce re
cord
s, w
ork
orde
rs, o
pera
tor l
ogs)
G
ener
ic e
vent
dat
a an
d pa
ram
eter
est
imat
es (e
.g.,
NU
RE
G/C
R-2
770,
EP
RI N
P-3
967)
N
RC
/INL
CC
F da
taba
se (N
UR
EG
/CR
-626
8)
2525
Dat
aA
naly
sis
Proc
ess
Dat
a A
naly
sis
Proc
ess
E
xam
ines
failu
re e
vent
s(no
t all
dem
ands
or
tsu
cces
s ev
ents
)
R
elat
ivel
yfe
wfa
ilure
sar
ecl
ear-
cutC
CFs
Rel
ativ
ely
few
failu
res
are
clea
rcu
t CC
Fs
Dem
ands
on
redu
ndan
t com
pone
nts
do n
ot a
lway
s oc
cur
sim
ulta
neou
sly
“F
ailu
res”
are
som
etim
es n
ot d
emon
stra
ted
failu
res
•S
econ
d co
mpo
nent
insp
ecte
d an
d re
veal
ed s
imila
r de
grad
atio
n/co
nditi
ons
In
terp
reta
tion
and
judg
men
tuse
dto
“fill-
in”t
heIn
terp
reta
tion
and
judg
men
t use
d to
fill
in th
e ga
ps in
the
data
D
egra
datio
n Va
lue
tech
niqu
e•
Ass
igns
prob
abili
ties
forl
ikel
ihoo
dan
even
twas
anac
tual
CC
FA
ssig
ns p
roba
bilit
ies
for l
ikel
ihoo
d an
eve
nt w
as a
n ac
tual
CC
F ev
ent
2626
Dat
aA
naly
sis
Proc
ess
Dat
a A
naly
sis
Proc
ess
C
lass
ifica
tion
exam
ple
Cla
ssifi
catio
n ex
ampl
ePl
ant T
ype
(Dat
e)Ev
ent D
escr
iptio
nIm
pact
Vec
tor
Com
pone
ntG
roup
Siz
eG
roup
Siz
e
To
mot
ordr
ien
AFW
pm
ps
PP 11
PP 22
00
PP
PWR
((12/
73))
Two
mot
or-d
riven
AFW
pum
psw
ere
inop
erab
le d
ue to
air
inco
mm
on s
uctio
n lin
e22
0000
11
D
ata
typi
cally
col
lect
ed in
clud
e
Com
pone
nt g
roup
siz
e
Num
ber o
f com
pone
nts
affe
cted
S
hock
type
(let
hal v
s. n
on-le
thal
)
Failu
re m
ode
2727
Dat
aA
naly
sis
Proc
ess
Dat
a A
naly
sis
Proc
ess
2828
Dat
aA
naly
sis
Proc
ess
Dat
a A
naly
sis
Proc
ess
C
CF
Bas
icE
vent
Qua
ntifi
catio
nC
CF
Bas
ic E
vent
Qua
ntifi
catio
n
M
onte
Car
lo m
etho
ds c
an b
e us
ed to
pro
paga
te
unce
rtain
ties
N
ote
that
sta
te o
f kno
wle
dge
depe
nden
ce is
lost
if th
e C
CF
basi
cev
ents
{AB
}{A
C}
{BC
}an
d{A
BC
}are
CC
F ba
sic
even
ts {A
B},
{AC
}, {B
C},
and
{AB
C} a
re
treat
ed a
s in
depe
nden
t bas
ic e
vent
s
2929
CC
F Ev
ent C
lass
ifica
tion
dA
li
San
d A
naly
sis
Sum
mar
y
3030
The
End
Que
stio
ns &
Ans
wer
s…..
3131