Datasets:
akumar33
/
manufacturing

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control E-CONPRI
( O
SPC S-CONPRI
) O
approach O
to O
detect O
process S-CONPRI
changes O
as S-MATE
soon O
as S-MATE
it O
occurs O
based O
on O
predefined O
distribution S-CONPRI
of O
the O
monitoring O
statistics S-CONPRI
. O
There O
are O
two O
major O
challenges O
: O
1 O
) O
complex O
spatial B-CONPRI
interdependence E-CONPRI
exists O
in O
the O
thermal B-FEAT
images E-FEAT
and O
current O
engineering B-CONPRI
knowledge E-CONPRI
is O
not O
sufficient O
to O
describe O
all O
the O
variability S-CONPRI
, O
and O
2 O
) O
the O
thermal B-FEAT
images E-FEAT
suffer O
from O
a O
large O
data S-CONPRI
volume O
, O
a O
low O
signal-to-noise B-PARA
ratio E-PARA
, O
and O
an O
ill O
structure S-CONPRI
with O
missing O
data S-CONPRI
. O
To O
tackle O
these O
challenges O
, O
multilinear B-CONPRI
principal I-CONPRI
component I-CONPRI
analysis E-CONPRI
( O
MPCA S-CONPRI
) O
approach O
is O
used O
to O
extract O
low O