I
nte
rna
t
io
na
l J
o
urna
l o
f
Appl
ied P
o
wer
E
ng
i
neer
ing
(
I
J
AP
E
)
Vo
l.
14
,
No
.
4
,
Dec
em
b
er
20
25
,
p
p
.
8
7
0
~
8
7
8
I
SS
N:
2252
-
8
7
9
2
,
DOI
:
1
0
.
1
1
5
9
1
/ijap
e
.
v
14
.
i
4
.
pp
870
-
878
870
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//
ija
p
e.
ia
esco
r
e.
co
m/
A hybrid
framew
o
rk of
Io
T
a
nd m
a
chine learning
for
predictive
ana
ly
tics of a DC
mo
tor
L
a
litha
K
a
nd
a
s
a
m
y
,
Anna
po
o
ra
ni G
a
nes
a
n,
M
.
Sh
un
m
ug
a
t
ha
mm
a
l
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
o
n
i
c
s a
n
d
C
o
mm
u
n
i
c
a
t
i
o
n
En
g
i
n
e
e
r
i
n
g
,
S
R
M
I
n
st
i
t
u
t
e
o
f
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
R
a
m
a
p
u
r
a
m C
a
m
p
u
s
,
C
h
e
n
n
a
i
,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
an
2
7
,
2
0
2
5
R
ev
is
ed
Au
g
2
0
,
2
0
2
5
Acc
ep
ted
Oct
1
6
,
2
0
2
5
M
a
n
y
i
n
d
u
strial
a
p
p
l
ica
ti
o
n
s u
ti
li
z
e
d
irec
t
c
u
rre
n
t
(
DC
)
m
o
to
r
a
s
a
n
e
ss
e
n
ti
a
l
e
lem
e
n
t.
It
fu
n
c
ti
o
n
s
a
s
t
h
e
b
a
c
k
b
o
n
e
o
f
se
v
e
ra
l
in
d
u
str
ies
a
n
d
g
l
o
b
a
l
p
il
lar
o
f
m
a
n
u
fa
c
tu
ri
n
g
a
p
p
l
ica
ti
o
n
s.
T
h
e
p
re
d
icti
v
e
a
n
a
ly
t
ics
o
f
m
o
t
o
r
is
p
rima
ry
fo
r
p
re
v
e
n
ti
n
g
u
n
p
re
d
icte
d
d
o
wn
ti
m
e
,
re
d
u
c
in
g
p
r
o
tec
ti
o
n
c
o
sts,
a
n
d
imp
ro
v
i
n
g
s
y
ste
m
e
ffe
c
ti
v
e
n
e
ss
.
Th
is
p
a
p
e
r
p
re
se
n
ts
a
h
y
b
rid
f
ra
m
e
wo
rk
in
teg
ra
ti
n
g
th
e
i
n
tern
e
t
o
f
t
h
i
n
g
s
(Io
T)
a
n
d
m
a
c
h
in
e
lea
rn
in
g
(M
L
)
fo
r
re
a
l
-
ti
m
e
p
re
d
ictiv
e
a
n
a
ly
ti
c
s
o
f
DC
m
o
to
rs.
T
h
e
lev
e
ra
g
i
n
g
o
f
m
a
c
h
i
n
e
lea
rn
in
g
a
lg
o
rit
h
m
s
in
p
re
d
ictiv
e
m
a
in
ten
a
n
c
e
o
f
DC
m
o
to
rs
h
a
s
sh
o
wn
s
ig
n
ifi
c
a
n
t
p
o
ten
ti
a
l
i
n
re
d
u
c
in
g
d
o
wn
ti
m
e
a
n
d
i
n
c
re
a
sin
g
th
e
li
fe
sp
a
n
o
f
t
h
e
m
o
to
r
.
Th
e
re
fo
re
,
a
sy
ste
m
fo
r
p
re
d
ictiv
e
a
n
a
ly
ti
c
s
with
m
a
c
h
in
e
lea
rn
in
g
stra
teg
y
is
p
ro
p
o
se
d
a
n
d
m
e
ss
a
g
e
q
u
e
u
i
n
g
tele
m
e
try
tran
sp
o
rt
(M
QTT
m
e
ss
a
g
in
g
)
i
s
in
c
lu
d
e
d
f
o
r
e
ffe
c
ti
v
e
i
n
fo
rm
a
ti
o
n
tran
sm
issio
n
b
e
twe
e
n
se
n
so
rs
a
n
d
g
a
tew
a
y
s.
Th
e
d
a
ta
re
c
e
iv
e
d
fro
m
th
e
se
n
so
rs
is
u
ti
li
z
e
d
to
m
a
k
e
p
re
d
ictio
n
a
b
o
u
t
th
e
re
m
a
in
i
n
g
u
se
fu
l
l
if
e
o
f
th
e
m
o
to
r
a
n
d
g
e
n
e
ra
te
a
lerts
fo
r
m
a
in
ten
a
n
c
e
b
e
fo
re
fa
il
u
re
s
o
c
c
u
r.
S
o
,
th
e
in
teg
ra
ti
o
n
o
f
m
a
c
h
i
n
e
lea
rn
in
g
a
lg
o
rit
h
m
s
in
p
re
d
ictiv
e
m
a
in
ten
a
n
c
e
o
f
DC
m
o
to
rs
is
a
p
ro
m
isin
g
a
p
p
ro
a
c
h
to
i
n
c
re
a
se
th
e
re
li
a
b
il
it
y
a
n
d
e
fficie
n
c
y
o
f
DC
m
o
t
o
rs.
T
h
e
h
i
g
h
e
st
p
e
rfo
rm
a
n
c
e
is ac
h
iev
e
d
i
n
ra
n
d
o
m
fo
re
st wi
th
a
c
c
u
ra
c
y
o
f
9
3
.
4
%
.
K
ey
w
o
r
d
s
:
DC
m
o
to
r
Fau
lt d
etec
tio
n
I
n
ter
n
et
o
f
th
in
g
s
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
Pre
d
ictiv
e
m
ain
ten
an
ce
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
M.
Sh
u
n
m
u
g
ath
am
m
al
Dep
ar
tm
en
t
o
f
E
lectr
o
n
ics an
d
C
o
m
m
u
n
icatio
n
E
n
g
in
ee
r
i
n
g
SR
M
I
n
s
titu
te
o
f
Scien
ce
an
d
T
ec
h
n
o
lo
g
y
,
R
am
ap
u
r
am
C
a
m
p
u
s
R
am
ap
u
r
am
,
C
h
en
n
ai,
T
am
il
Nad
u
,
I
n
d
ia
E
m
ail:
s
h
u
m
m
y
b
@
g
m
ail.
co
m
or
s
h
au
n
m
u
m
@
s
r
m
is
t.e
d
u
.
in
1.
I
NT
RO
D
UCT
I
O
N
Dir
ec
t
cu
r
r
en
t
(
DC
)
m
o
to
r
s
ar
e
m
u
ltifa
r
io
u
s
elec
tr
o
-
m
ec
h
a
n
ical
d
ev
ices
wh
ich
tr
an
s
f
o
r
m
elec
tr
ical
en
er
g
y
in
to
m
ec
h
an
ical
e
n
er
g
y
.
C
o
m
m
o
n
DC
m
o
to
r
ap
p
lic
atio
n
s
in
clu
d
e
p
u
m
p
s
,
m
ac
h
in
er
ies
in
au
to
m
o
b
ile
in
d
u
s
tr
ies,
ce
n
tr
if
u
g
al
m
a
ch
in
es,
m
ac
h
in
e
to
o
ls
,
c
o
n
v
e
y
o
r
s
,
an
d
elev
ato
r
s
[
1
]
.
On
th
e
o
th
er
h
a
n
d
,
m
ac
h
in
e
r
y
in
co
al
p
lan
ts
,
g
r
ain
elev
ato
r
s
,
an
d
s
h
r
ed
d
er
s
u
s
e
DC
m
o
to
r
s
in
d
an
g
er
o
u
s
s
ettin
g
s
,
as
d
o
p
etr
o
ch
em
ical
an
d
n
atu
r
al
g
as
p
lan
ts
[
2
]
.
No
r
m
al
ly
,
elec
tr
ical
an
d
m
ec
h
an
ical
t
r
o
u
b
les
ca
n
ca
u
s
e
a
m
o
to
r
to
f
ail
[
3
]
.
Me
ch
an
ical
lo
ad
s
ca
u
s
ed
b
y
o
v
er
lo
a
d
s
an
d
ab
r
u
p
t
lo
a
d
s
h
if
ts
ca
n
r
esu
lt
in
b
ea
r
in
g
f
ailu
r
e
an
d
r
o
to
r
b
ar
r
u
p
tu
r
e.
On
th
e
o
th
er
h
a
n
d
,
elec
tr
ical
s
tr
ain
s
ar
e
ty
p
ically
c
o
n
n
ec
te
d
to
t
h
e
p
o
wer
s
o
u
r
ce
.
W
h
en
p
o
w
er
ed
b
y
alter
n
atin
g
cu
r
r
en
t
(
AC
)
d
r
iv
es,
DC
m
o
to
r
s
ar
e
m
o
r
e
p
r
o
n
e
to
f
ailu
r
e.
DC
m
o
to
r
s
ar
e
in
cr
ed
i
b
ly
tr
u
s
two
r
th
y
d
ev
ices,
esp
ec
ially
s
q
u
ir
r
el
ca
g
e
DC
m
o
to
r
s
.
Du
e
t
o
i
ts
n
atu
r
e,
th
is
m
o
to
r
m
ay
f
u
n
ctio
n
in
a
r
an
g
e
o
f
ch
alle
n
g
in
g
cir
c
u
m
s
tan
ce
s
.
I
t
m
ay
h
av
e
a
n
u
m
b
er
o
f
d
e
f
ec
ts
o
r
an
o
m
alies
[
4
]
.
T
h
e
m
o
s
t
co
m
m
o
n
d
ef
ec
ts
ar
e
f
a
u
lts
in
th
e
b
ea
r
in
g
,
s
tato
r
,
an
d
r
o
t
o
r
.
Ma
n
y
o
f
t
h
ese
d
ef
ec
ts
h
av
e
n
eg
ativ
e
ef
f
ec
ts
o
f
t
h
eir
o
wn
.
T
h
ese
s
id
e
e
f
f
ec
ts
tu
r
n
d
an
g
er
o
u
s
f
o
r
th
e
e
n
g
in
e
if
th
e
f
au
lt
is
n
o
t
f
ix
e
d
.
T
h
er
ef
o
r
e,
it'
s
cr
u
cial
to
ad
d
r
e
s
s
a
p
r
o
b
lem
as
q
u
ick
ly
as
p
o
s
s
ib
le.
T
h
e
g
r
ea
ter
v
o
ltag
e
te
n
s
io
n
o
n
th
e
s
tato
r
's
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
A
h
yb
r
id
fr
a
mewo
r
k
o
f
I
o
T
a
n
d
ma
ch
in
e
le
a
r
n
in
g
fo
r
p
r
ed
ict
ive
a
n
a
lytics o
f
…
(
La
lith
a
K
a
n
d
a
s
a
my
)
871
win
d
in
g
s
,
th
e
h
i
g
h
er
f
r
e
q
u
en
c
y
o
f
th
e
s
tato
r
c
u
r
r
e
n
t
elem
e
n
ts
,
an
d
th
e
s
tim
u
lated
b
ea
r
i
n
g
cu
r
r
en
ts
ca
u
s
ed
b
y
AC
d
r
iv
es
ar
e
th
e
ca
u
s
e
s
o
f
t
h
is
.
Ad
d
itio
n
ally
,
m
o
to
r
o
v
er
v
o
ltag
es
ca
n
h
ap
p
en
b
ec
a
u
s
e
o
f
th
e
len
g
th
o
f
th
e
ca
b
le
co
n
n
ec
tin
g
th
e
m
o
to
r
to
th
e
alter
n
atin
g
cu
r
r
en
t
(
AC
)
d
r
iv
e.
T
em
p
er
at
u
r
e,
cu
r
r
e
n
t,
a
n
d
v
ib
r
atio
n
s
en
s
o
r
s
ar
e
u
s
ed
b
y
elec
tr
ical
m
o
to
r
s
t
o
co
llect
d
ata
in
r
ea
l
tim
e.
T
h
e
d
ata
is
th
en
ev
alu
ated
an
d
f
a
ilu
r
es
ar
e
p
r
ed
icted
u
s
in
g
m
ac
h
in
e
lea
r
n
in
g
m
o
d
el
s
[
5
]
,
[
6
]
.
I
n
d
u
s
tr
y
4
.
0
tech
n
o
lo
g
y
h
as
th
e
p
o
ten
tial
to
cr
ea
te
s
y
s
tem
s
f
o
r
co
n
d
itio
n
-
b
ased
m
o
n
i
to
r
in
g
an
d
p
r
ed
ictiv
e
m
ain
ten
an
ce
b
y
in
t
eg
r
atin
g
ar
tific
ial
in
tellig
en
ce
(
AI
)
,
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
,
b
ig
d
ata
an
aly
tics
,
an
d
cy
b
er
p
h
y
s
ical
s
y
s
tem
s
[
7
]
,
[
8
]
.
Pre
d
ictiv
e
an
aly
tics
is
a
n
ef
f
icien
t
t
o
o
l
f
o
r
c
o
n
d
itio
n
m
o
n
ito
r
in
g
[
9
]
.
T
o
f
in
d
f
laws,
th
e
DC
m
o
to
r
's
c
u
r
r
en
t
an
d
v
ib
r
atio
n
wer
e
m
o
n
ito
r
ed
.
Vib
r
atio
n
s
p
ec
tr
u
m
an
aly
s
is
i
s
u
s
ed
to
id
en
tify
m
ec
h
an
ical
p
r
o
b
lem
s
lik
e
b
ea
r
in
g
d
am
ag
e,
wh
er
ea
s
th
e
c
u
r
r
e
n
t
an
al
y
s
is
is
u
s
ed
t
o
id
en
tif
y
elec
tr
ical
f
au
lts
lik
e
s
in
g
le
p
h
ase
with
u
n
b
alan
ce
d
s
u
p
p
ly
.
T
h
e
r
elatio
n
b
etwe
en
elec
tr
ical
p
a
r
am
ete
r
s
(
v
o
ltag
e,
c
u
r
r
e
n
t,
an
d
tem
p
er
atu
r
e
)
an
d
m
ec
h
an
ical
p
r
o
p
er
ties
s
u
ch
as
an
ai
r
f
lo
w
f
r
o
m
t
h
e
b
l
o
wer
,
a
n
d
b
r
u
s
h
len
g
th
ex
ac
tly
r
ev
ea
ls
ab
o
u
t
th
e
o
p
er
atin
g
co
n
d
itio
n
s
o
f
DC
m
o
to
r
[
9
]
.
Mo
s
t
o
f
th
e
m
an
u
f
ac
tu
r
in
g
in
d
u
s
t
r
ies
u
s
e
p
r
ed
ictiv
e
m
ain
ten
an
ce
to
im
p
r
o
v
e
th
eir
ca
p
ac
ity
an
d
q
u
ality
.
Ma
ch
in
e
s
p
r
o
d
u
ce
h
u
g
e
am
o
u
n
t
o
f
d
at
a
an
d
all
th
ese
d
ata
to
b
e
co
n
v
er
ted
to
v
alu
a
b
le
an
d
ac
tio
n
a
b
le
in
f
o
r
m
atio
n
.
T
h
e
ch
allen
g
e
lies
in
in
ter
p
r
etin
g
t
h
e
d
ata
f
o
r
c
r
ea
tin
g
u
s
ef
u
l in
s
ig
h
t [
1
0
]
.
Data
an
aly
s
is
an
d
ML
a
lg
o
r
ith
m
s
ar
e
u
s
ed
in
p
r
ed
ictiv
e
m
ain
ten
an
ce
,
a
p
r
o
ac
tiv
e
m
a
in
ten
an
ce
s
tr
ateg
y
,
to
f
o
r
ec
ast
an
d
s
to
p
eq
u
ip
m
en
t
f
au
lts
b
ef
o
r
e
th
e
y
h
a
p
p
en
.
T
h
is
a
p
p
r
o
ac
h
r
ec
o
r
d
s
d
ata
o
n
ea
ch
co
m
p
o
n
en
t
f
u
n
ctio
n
in
g
an
d
c
ir
cu
m
s
tan
ce
s
,
allo
win
g
f
o
r
a
tr
en
d
an
aly
s
is
o
f
o
v
er
all
b
e
h
av
io
u
r
.
T
h
e
s
to
r
ed
in
f
o
r
m
atio
n
an
d
cu
r
r
en
t
d
ata
ar
e
u
s
ed
to
m
ak
e
in
tellig
en
t
d
ec
is
io
n
o
n
m
ain
te
n
an
ce
ac
tiv
i
ties
.
To
f
in
d
tr
e
n
d
s
an
d
an
o
m
alies
th
at
ca
n
p
o
in
t
t
o
a
f
u
tu
r
e
f
ailu
r
e,
th
ese
alg
o
r
ith
m
s
ca
n
ex
am
in
e
d
ata
f
r
o
m
a
v
ar
iety
o
f
s
o
u
r
ce
s
,
in
clu
d
in
g
h
is
to
r
ical
d
ata,
s
en
s
o
r
d
ata,
a
n
d
e
n
v
ir
o
n
m
en
tal
d
a
ta.
Fo
r
m
o
to
r
p
r
ed
ictiv
e
m
ain
t
en
an
ce
,
co
m
b
in
in
g
an
em
b
ed
d
ed
s
y
s
tem
with
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
h
as
a
n
u
m
b
er
o
f
b
e
n
ef
its
.
I
t f
ir
s
t p
r
o
v
id
es e
ar
ly
f
ailu
r
e
d
etec
tio
n
,
wh
ich
ca
n
c
u
t d
o
wn
tim
e
an
d
m
ain
ten
an
ce
c
o
s
ts
.
S
ec
o
n
d
,
b
y
id
en
tif
y
in
g
an
d
f
ix
i
n
g
p
r
o
b
lem
s
b
ef
o
r
e
th
ey
wo
r
s
en
,
it
ca
n
in
cr
ea
s
e
th
e
m
o
to
r
'
s
o
v
er
all
ef
f
ec
tiv
e
n
ess
an
d
p
er
f
o
r
m
an
ce
.
T
h
is
m
ay
lead
to
en
e
r
g
y
s
av
in
g
s
an
d
less
d
am
ag
e
to
th
e
en
g
in
e.
Fin
ally
,
b
y
m
in
im
izin
g
wea
r
an
d
tear
b
r
o
u
g
h
t
o
n
b
y
u
n
ex
p
ec
ted
f
ailu
r
es,
it
ca
n
aid
in
ex
ten
d
in
g
th
e
m
o
to
r
'
s
life
s
p
an
.
T
h
e
o
b
j
ec
tiv
e
is
to
d
esig
n
a
m
o
d
el
th
a
t
p
r
ed
icts
p
o
ten
tial
f
au
lt p
r
esen
t in
th
e
DC
m
o
to
r
.
Hash
em
ian
an
d
B
ea
n
[
1
0
]
d
es
cr
ib
ed
ab
o
u
t
th
e
r
o
le
o
f
o
n
lin
e
m
ain
ten
an
ce
an
d
ca
lib
r
atio
n
tech
n
iq
u
es
em
p
lo
y
ed
to
d
eter
m
in
e
th
e
lif
esp
an
o
f
in
d
u
s
tr
ial
m
ac
h
in
es.
Wu
et
a
l.
[
1
1
]
p
r
o
p
o
s
ed
a
m
eth
o
d
to
m
in
im
ize
co
s
t
in
v
o
lv
ed
i
n
p
r
e
d
ictiv
e
m
a
in
ten
an
ce
b
y
em
p
lo
y
in
g
n
eu
r
a
l
n
etwo
r
k
-
b
ased
p
r
ed
ictio
n
m
o
d
el.
L
u
et
a
l.
[
1
2
]
d
ev
elo
p
e
d
a
p
r
ev
en
tiv
e
m
ain
t
en
an
ce
tech
n
iq
u
e
b
ased
o
n
co
n
tin
u
o
u
s
m
o
n
ito
r
in
g
f
o
r
in
d
u
ctio
n
m
o
to
r
s
.
T
h
is
ap
p
r
o
ac
h
f
ails
in
r
ea
l
tim
e
m
o
n
ito
r
in
g
s
in
ce
it
d
o
es
co
n
s
id
er
p
ast
h
is
to
r
y
o
f
d
ata
[
1
3
]
.
L
u
et
a
l.
[
1
2
]
d
esig
n
ed
a
co
n
d
itio
n
-
b
ased
m
o
n
ito
r
in
g
to
m
in
im
ize
ec
o
n
o
m
ic
lo
s
s
es.
T
h
is
m
eth
o
d
s
u
g
g
ests
a
s
u
itab
le
tech
n
iq
u
e
ca
lle
d
n
o
n
in
tr
u
s
iv
e
m
o
to
r
-
ef
f
icien
c
y
esti
m
atio
n
[
1
4
]
.
I
n
d
u
s
tr
ial
ap
p
licatio
n
o
f
m
o
to
r
cu
r
r
en
t
s
ig
n
atu
r
e
an
al
y
s
is
f
o
r
f
au
lt
esti
m
atio
n
is
d
o
n
e.
T
h
e
v
ib
r
atio
n
s
en
s
o
r
d
ata
o
b
tain
ed
f
r
o
m
s
en
s
o
r
s
p
u
t
o
n
m
ac
h
in
es
to
m
o
n
it
o
r
cr
u
cial
co
m
p
o
n
e
n
ts
.
T
h
e
p
r
e
-
p
r
o
ce
s
s
ed
s
ig
n
als
wer
e
d
ec
o
m
p
o
s
ed
in
t
o
m
a
n
y
s
ig
n
als
co
m
p
r
is
in
g
o
n
e
a
p
p
r
o
x
im
atio
n
an
d
s
o
m
e
d
etails
u
s
in
g
wav
elet
p
ac
k
et
d
ec
o
m
p
o
s
itio
n
,
wh
ich
wer
e
t
h
en
tr
a
n
s
f
er
r
ed
to
t
h
e
f
r
eq
u
e
n
cy
d
o
m
ain
u
s
in
g
th
e
f
ast
F
o
u
r
ier
tr
an
s
f
o
r
m
[
1
5
]
.
T
h
e
f
ea
tu
r
es
r
etr
ie
v
ed
f
r
o
m
t
h
e
f
r
eq
u
en
cy
d
o
m
ai
n
co
u
l
d
b
e
u
tili
ze
d
to
tr
ain
an
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
.
T
r
ain
ed
a
r
tific
ial
n
eu
r
al
n
etwo
r
k
s
ca
n
a
n
ticip
ate
d
eg
r
ad
atio
n
(
r
em
ain
in
g
u
s
ef
u
l
life
)
an
d
d
is
co
v
er
f
au
lts
in
co
m
p
o
n
e
n
ts
an
d
eq
u
i
p
m
en
t [
1
6
]
.
A
n
o
n
-
in
v
asiv
e
m
eth
o
d
m
ea
s
u
r
in
g
v
ib
r
atio
n
d
at
a
f
o
r
p
r
e
d
ictio
n
an
d
d
iag
n
o
s
is
o
f
f
a
u
lt in
in
d
u
s
tr
ial
m
ac
h
in
es is
d
o
n
e
[
1
7
]
.
Un
al
et
a
l.
[
1
8
]
m
o
d
elled
a
n
eu
r
al
n
etwo
r
k
th
at
h
as
b
ee
n
tr
ain
ed
to
p
r
ed
ict
p
r
e
v
en
tiv
e
m
ain
ten
an
ce
u
s
in
g
s
en
s
o
r
d
ata.
T
h
e
v
ar
iatio
n
s
in
tem
p
er
atu
r
e,
b
atter
y
v
o
ltag
e,
an
d
b
r
ak
e
wea
r
ar
e
m
o
n
ito
r
ed
b
y
a
g
r
o
u
p
o
f
I
o
T
s
en
s
o
r
s
to
im
p
r
o
v
e
th
e
ef
f
icien
cy
o
f
p
r
e
d
ictiv
e
m
ain
ten
a
n
ce
[
1
9
]
.
Pre
d
ictiv
e
m
ain
ten
a
n
ce
o
f
m
ac
h
in
e
t
o
o
l
s
y
s
tem
s
is
d
o
n
e
u
s
in
g
a
r
tific
ial
in
tellig
en
ce
[
2
0
]
.
E
d
g
e
co
m
p
u
tin
g
a
n
d
cy
b
e
r
s
ec
u
r
ity
tec
h
n
i
q
u
es
ar
e
u
tili
ze
d
i
n
s
m
ar
t
m
an
u
f
ac
tu
r
in
g
f
o
r
I
n
d
u
s
tr
y
4
.
0
[
2
1
]
-
[
2
3
]
.
T
h
e
e
f
f
i
cien
cy
an
d
d
ep
e
n
d
ab
ilit
y
o
f
th
e
m
o
to
r
ca
n
b
e
in
cr
ea
s
ed
b
y
g
ath
er
i
n
g
s
en
s
o
r
d
ata,
u
tili
zin
g
m
ac
h
in
e
lear
n
in
g
m
o
d
els
to
in
ter
p
r
et
th
e
d
ata,
an
d
p
r
ed
ictin
g
wh
en
m
ain
ten
an
ce
is
n
ec
ess
ar
y
.
Mo
n
ito
r
in
g
a
m
o
to
r
’
s
wo
r
k
i
n
g
co
n
d
itio
n
an
d
p
o
s
s
ib
le
an
o
m
aly
id
en
tific
atio
n
ar
e
r
eq
u
ir
ed
b
ef
o
r
e
th
ey
b
ec
o
m
e
s
er
io
u
s
with
th
e
h
elp
o
f
p
r
e
d
ictiv
e
m
ai
n
ten
an
ce
[
2
4
]
,
[
2
5
]
.
T
h
is
p
ap
er
an
aly
s
es
s
o
m
e
ch
ar
ac
ter
is
tics
DC
m
o
to
r
s
u
ch
as
cu
r
r
e
n
t,
v
o
ltag
e,
tem
p
er
atu
r
e
,
s
p
ee
d
,
a
n
d
v
ib
r
atio
n
i
n
o
r
d
er
to
p
r
ed
ict
t
h
e
wo
r
k
in
g
co
n
d
itio
n
o
f
DC
m
o
to
r
.
T
h
e
DC
m
o
to
r
o
f
f
er
s
s
o
m
e
f
le
x
ib
ilit
y
in
m
ea
s
u
r
in
g
e
x
ter
n
al
b
lo
wer
cu
r
r
e
n
t,
s
p
ee
d
m
ea
s
u
r
em
en
t,
an
d
tem
p
e
r
atu
r
e
m
ea
s
u
r
em
en
t
(
co
n
tr
o
l
p
an
el
an
d
a
m
b
ien
t
tem
p
er
atu
r
e
)
.
E
r
r
o
r
s
in
d
icate
d
in
a
b
o
v
e
m
e
n
tio
n
ed
p
ar
am
eter
s
in
d
icate
th
e
p
r
esen
ce
o
f
p
o
ten
tial
is
s
u
es
wh
ich
h
av
e
to
b
e
ad
d
r
ess
ed
im
m
ed
iately
to
co
n
tin
u
e
th
e
n
o
r
m
al
wo
r
k
in
g
c
o
n
d
itio
n
.
T
h
e
m
ai
n
th
em
e
o
f
th
e
wo
r
k
with
th
e
s
p
ec
if
ic
g
o
als
o
f
th
e
d
esig
n
ar
e
g
iv
e
n
b
el
o
w
:
i)
r
eal
-
tim
e
ac
q
u
is
itio
n
o
f
m
o
to
r
p
ar
a
m
eter
s
(
e.
g
.
,
s
p
ee
d
,
v
o
ltag
e,
c
u
r
r
e
n
t,
tem
p
er
atu
r
e
)
th
r
o
u
g
h
I
o
T
-
e
n
ab
led
s
en
s
o
r
s
;
i
i)
p
re
-
p
r
o
ce
s
s
in
g
an
d
tr
an
s
m
is
s
io
n
o
f
th
is
d
ata
to
a
ce
n
tr
al
o
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iii)
Ap
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p
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ML
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ith
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ANN)
to
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tates,
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
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9
2
I
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t J Ap
p
l Po
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n
g
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Vo
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14
,
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4
,
Dec
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b
er
20
25
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7
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2.
M
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o
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ith
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to
p
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ed
ict
t
h
e
c
o
n
d
itio
n
o
f
m
o
to
r
.
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h
e
h
ar
d
war
e
ar
r
an
g
em
e
n
t
n
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ce
s
s
ar
y
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th
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llectio
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h
e
s
en
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o
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d
ata
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s
h
o
wn
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Fi
g
u
r
e
1
.
T
h
e
m
o
to
r
'
s
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io
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r
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d
d
ata
co
llectio
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im
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lem
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ted
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s
in
g
a
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o
r
s
s
u
c
h
as
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em
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en
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r
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v
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en
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r
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v
o
ltag
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en
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o
r
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d
cu
r
r
en
t sen
s
o
r
.
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h
e
m
ec
h
an
ical
an
d
elec
tr
ica
l
p
ar
am
eter
s
ass
o
ciate
d
with
th
e
o
p
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atio
n
o
f
th
e
m
o
to
r
a
n
d
o
v
er
all
co
n
d
itio
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o
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m
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r
was
t
h
o
r
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g
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ly
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al
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u
s
in
g
t
h
is
m
u
lti
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en
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o
r
ap
p
r
o
ac
h
.
T
h
is
p
r
o
p
o
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ed
ap
p
r
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h
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llects
th
e
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ata
au
to
m
atica
ll
y
f
r
o
m
a
v
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o
f
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en
s
o
r
s
an
d
p
r
e
-
p
r
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ce
s
s
in
g
th
em
.
Su
b
s
e
q
u
en
tly
,
it
p
er
f
o
r
m
s
f
ea
tu
r
e
ex
t
r
ac
tio
n
,
m
o
d
el
b
u
il
d
in
g
,
t
r
ain
in
g
,
v
alid
atio
n
,
an
d
d
ep
lo
y
m
en
t
t
o
cr
ea
te
a
p
r
ed
i
ctiv
e
m
ain
ten
an
ce
m
o
d
el.
Data
c
o
llectio
n
is
th
e
p
r
im
ar
y
p
ar
t
o
f
an
au
to
m
atic
m
o
n
ito
r
in
g
s
y
s
tem
th
at
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ata
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llectio
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f
r
o
m
d
if
f
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en
t sen
s
o
r
s
,
s
u
ch
as v
ib
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n
,
tem
p
er
atu
r
e,
cu
r
r
en
t,
a
n
d
v
o
ltag
e.
T
h
e
DS1
8
B
2
0
tem
p
er
at
u
r
e
s
en
s
o
r
was
em
p
l
o
y
ed
to
ac
q
u
ir
e
tem
p
er
atu
r
e
d
ata.
I
t
s
im
p
lifie
s
th
e
wir
in
g
co
m
p
le
x
ity
b
y
e
n
ab
lin
g
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e
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n
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ec
tio
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o
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u
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le
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en
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o
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s
o
n
a
s
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le
d
ata
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e
t
h
r
o
u
g
h
th
e
o
n
e
-
wir
e
in
ter
f
ac
e.
I
t
is
ca
p
ab
le
o
f
m
ea
s
u
r
in
g
tem
p
er
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r
es
with
in
th
e
r
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g
e
o
f
-
55
°C
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2
5
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with
a
r
eso
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tio
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u
p
to
1
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ig
its
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h
e
AC
S7
1
2
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r
r
en
t
s
en
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r
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a
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en
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ates
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Hall
ef
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ec
t
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r
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t
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eq
u
ip
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a
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esis
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ce
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r
r
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o
n
d
u
cto
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an
d
a
2
.
1
k
V
R
MS
v
o
ltag
e
is
o
latio
n
ca
p
ab
ilit
y
.
Usi
n
g
a
m
icr
o
co
n
tr
o
ller
-
b
ased
d
ata
co
llectio
n
s
y
s
tem
an
d
a
co
m
b
in
atio
n
o
f
s
en
s
o
r
s
,
t
h
is
m
eth
o
d
o
lo
g
y
f
ac
ilit
ated
an
ex
h
au
s
tiv
e
an
d
d
etailed
a
n
aly
s
is
o
f
th
e
m
o
to
r
'
s
p
er
f
o
r
m
an
c
e
an
d
h
ea
lth
.
Var
io
u
s
o
th
er
t
y
p
es
o
f
s
en
s
o
r
s
h
a
v
e
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ee
n
em
p
lo
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e
d
to
g
at
h
er
v
i
b
r
atio
n
d
ata
f
r
o
m
th
e
s
y
s
tem
.
T
h
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en
s
o
r
s
in
clu
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e
a
cc
eler
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eter
s
en
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r
(X
-
NUCLE
O
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)
,
m
ic
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,
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s
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en
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I
I
S2
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ax
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ag
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I
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ax
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lt
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it p
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ter
f
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Ar
d
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icr
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o
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²C
p
in
.
Fig
u
r
e
1
.
Har
d
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d
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f
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ed
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e
a
n
aly
tics
m
o
d
el
2
.
1
.
Dev
el
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pm
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a
predict
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ly
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ing
m
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chine le
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AI
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y
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tr
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s
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p
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lectr
ical
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o
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u
s
e
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ib
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,
cu
r
r
en
t,
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atu
r
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s
en
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o
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s
to
co
llect
d
ata
in
r
ea
l
ti
m
e.
T
h
e
d
ata
is
th
en
ev
alu
ated
an
d
f
ailu
r
es
ar
e
p
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ed
icted
u
s
in
g
f
o
u
r
m
ac
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lear
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m
o
d
els:
k
-
n
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r
est
n
ei
g
h
b
o
u
r
(
KNN)
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
r
an
d
o
m
f
o
r
est
(
R
F),
a
n
d
l
i
n
e
a
r
r
e
g
r
e
s
s
i
o
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(
L
R
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.
M
e
s
s
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g
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e
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m
m
u
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et
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n
s
en
s
o
r
s
,
g
atew
ay
s
,
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d
clo
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er
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s
.
W
h
en
it
co
m
es
to
wo
r
k
in
g
m
o
t
o
r
s
,
RF
d
is
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lay
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th
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ig
h
est p
r
ec
is
io
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d
o
p
tim
is
es m
ain
ten
an
ce
s
ch
ed
u
les to
r
ed
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ce
ex
p
e
n
s
es a
n
d
d
o
w
n
tim
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
A
h
yb
r
id
fr
a
mewo
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k
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f
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o
T
a
n
d
ma
ch
in
e
le
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r
n
in
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fo
r
p
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ed
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ive
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n
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lytics o
f
…
(
La
lith
a
K
a
n
d
a
s
a
my
)
873
T
h
e
p
r
ed
ictio
n
p
r
o
ce
s
s
is
d
o
n
e
b
y
AI
al
g
o
r
ith
m
s
th
at
e
n
tails
th
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n
g
o
f
p
atter
n
s
f
r
o
m
d
ata,
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d
th
e
m
ak
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g
o
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ec
is
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s
with
m
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al
o
v
er
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ig
h
t
as
r
ep
r
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ted
in
Fig
u
r
e
2
.
C
o
n
clu
s
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n
s
ar
e
s
u
b
s
eq
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en
tly
m
ad
e
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ased
o
n
th
e
n
ew
d
ata
an
d
p
ast
h
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to
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y
u
s
in
g
th
ese
m
o
d
els.
Data
co
llectio
n
is
d
o
n
e
d
u
r
in
g
n
o
r
m
al
an
d
f
au
lt
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n
d
itio
n
s
.
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h
e
d
ataset
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d
iv
id
ed
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th
r
ee
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ar
ts
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o
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tr
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d
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f
o
r
v
alid
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n
)
m
ac
h
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e
lear
n
in
g
b
ased
p
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ed
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e
m
ai
n
ten
an
ce
attem
p
ts
to
lear
n
f
r
o
m
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llected
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ata
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b
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n
d
p
ast ex
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s
)
t
o
d
is
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v
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y
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tem
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ailu
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o
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h
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ata
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clea
n
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y
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r
e
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p
r
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s
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ay
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ilter
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s
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o
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m
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et
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tr
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tr
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ct
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r
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r
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m
th
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ata.
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q
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o
m
ai
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etr
ic
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ea
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tan
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ar
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e
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k
u
r
to
s
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e
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tr
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ted
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h
e
f
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e
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tr
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ti
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n
en
a
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les
th
e
m
ac
h
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m
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el
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ize
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ata
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atter
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th
at
ar
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a
n
in
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icativ
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o
f
d
is
o
r
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er
s
o
f
m
o
to
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h
ea
lth
.
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h
is
ca
n
b
e
ac
c
o
m
p
lis
h
ed
u
s
in
g
a
v
a
r
iety
o
f
m
ac
h
in
e
lear
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i
n
g
tech
n
iq
u
es,
s
u
ch
as
KNN,
S
V
M,
R
F,
an
d
L
R
.
T
h
e
p
r
e
-
p
r
o
c
ess
ed
d
ata
an
d
th
e
ex
tr
ac
ted
f
ea
tu
r
es
ar
e
u
s
ed
to
tr
ain
th
e
co
n
s
tr
u
cted
m
o
d
el.
T
h
e
tr
ain
in
g
d
ata
h
as
to
h
av
e
illu
s
tr
atio
n
s
o
f
b
o
th
h
ea
lth
y
an
d
f
au
lty
m
o
to
r
co
n
d
itio
n
s
.
A
d
if
f
er
en
t
s
et
o
f
d
ata
th
at
wasn
'
t
u
ti
lized
f
o
r
tr
ain
in
g
is
u
s
ed
to
v
alid
ate
th
e
m
o
d
el
af
ter
i
t
h
as
b
ee
n
tr
ain
ed
.
T
h
is
m
ak
es
it
m
o
r
e
lik
ely
th
at
th
e
m
o
d
el
will
co
r
r
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tly
ca
teg
o
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ies
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r
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d
-
n
ew
d
ata
th
at
it
h
as
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er
s
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n
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ef
o
r
e.
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h
e
v
alid
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n
o
f
th
e
m
o
d
el
is
a
cr
u
cial
s
tag
e
s
in
ce
it
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ar
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r
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ep
en
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d
ab
ilit
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ately
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h
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ied
m
o
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is
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en
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b
ed
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e
d
in
to
th
e
m
icr
o
co
n
tr
o
ller
f
o
r
r
ea
l
-
tim
e
m
o
to
r
h
ea
lth
m
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ito
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g
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Py
th
o
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an
d
th
e
p
y
Ser
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r
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y
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tili
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co
n
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with
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e
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ial
d
ev
ice
an
d
r
etr
iev
e
th
e
d
ata
tr
an
s
m
itted
th
r
o
u
g
h
th
e
s
er
ial
p
o
r
t
in
ter
f
ac
e.
T
h
e
p
y
Ser
ial
lib
r
ar
y
p
r
o
v
id
es
u
s
er
-
f
r
ien
d
l
y
f
u
n
ctio
n
f
o
r
th
e
m
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a
g
em
en
t
o
f
s
er
ial
co
m
m
u
n
icatio
n
p
r
o
to
co
ls
.
A
Py
th
o
n
s
cr
ip
t
e
s
tab
lis
h
es
a
s
er
ial
co
n
n
ec
tio
n
with
th
e
tar
g
et
d
e
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ice,
s
p
ec
if
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in
g
th
e
n
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ess
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y
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ar
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eter
s
,
in
cl
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d
in
g
th
e
p
o
r
t
an
d
b
au
d
r
ate,
in
o
r
d
er
to
c
o
llect
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ata.
Up
o
n
est
ab
lis
h
in
g
th
e
co
n
n
ec
tio
n
,
th
e
s
cr
ip
t
ex
ec
u
tes
a
lo
o
p
th
at
co
n
tin
u
o
u
s
ly
r
etr
iev
es
d
ata
f
r
o
m
t
h
e
s
er
ial
p
o
r
t.
T
h
e
d
ata
th
at
is
ex
tr
ac
ted
f
r
o
m
t
h
e
s
er
ial
in
ter
f
ac
e
is
ty
p
ically
in
b
in
ar
y
o
r
s
tr
in
g
f
o
r
m
at
an
d
is
co
n
s
is
ten
t
with
th
e
d
ev
ice'
s
p
r
o
to
co
l.
E
ac
h
d
a
ta
p
o
in
t,
wh
ich
d
e
n
o
tes
a
m
ea
s
u
r
em
en
t
f
r
o
m
th
e
s
er
ial
d
ev
ice,
is
r
ec
o
r
d
ed
as a
r
o
w
in
a
C
SV
f
ile,
with
ea
ch
p
ar
am
eter
d
esig
n
ated
to
its
co
r
r
esp
o
n
d
in
g
co
l
u
m
n
.
T
h
e
m
o
d
el
c
o
n
tin
u
o
u
s
ly
e
x
a
m
in
es
th
e
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d
ata
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n
o
tifie
s
th
e
o
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ato
r
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n
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o
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alies
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r
ap
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r
o
ac
h
in
g
f
ailu
r
es
b
y
s
en
d
i
n
g
a
n
alar
m
.
T
h
e
o
b
jectiv
e
is
to
cr
ea
te
a
p
r
ec
is
e
an
d
tr
u
s
two
r
th
y
m
o
d
e
l
th
at
ca
n
id
en
tif
y
m
o
to
r
f
laws b
ef
o
r
e
th
e
y
r
esu
lt
in
ex
p
e
n
s
iv
e
d
o
wn
tim
e
o
r
r
e
p
air
s
.
Fig
u
r
e
2
.
B
lo
ck
d
iag
r
am
o
f
m
ac
h
in
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lear
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ted
p
r
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n
aly
tics
m
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el
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Ana
ly
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is
o
f
v
ibra
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n si
g
na
l
T
h
e
v
ib
r
atio
n
o
f
th
e
m
o
to
r
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m
o
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est
in
am
p
litu
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e,
with
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r
h
y
th
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ic
in
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r
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e
in
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p
litu
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r
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h
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m
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s
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h
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ic
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r
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es
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u
r
in
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b
u
b
b
lin
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p
r
o
ce
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s
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eter
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f
ix
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o
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th
e
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ea
r
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g
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e
o
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o
r
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1
0
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e
co
n
d
s
.
Fas
t
Fo
u
r
ier
tr
an
s
f
o
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m
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s
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lcu
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en
s
ity
f
r
o
m
r
aw
d
ata
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am
p
les.
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h
e
s
eq
u
en
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o
f
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aw
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ata
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am
p
les
tak
en
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m
DC
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t
o
r
in
th
e
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o
r
m
o
f
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o
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r
ier
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er
ies,
it
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ld
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e
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r
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k
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o
wn
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in
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e
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m
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l
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o
r
d
e
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o
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al
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is
,
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ch
f
r
eq
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en
c
y
co
m
p
o
n
e
n
t is "te
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ted
"
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o
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p
r
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ce
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h
e
r
aw
wav
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o
r
m
is
m
u
ltip
lied
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y
s
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e
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es
o
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is
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r
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en
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s
in
g
a
d
is
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et
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ie
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tr
an
s
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atch
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ch
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FF
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o
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to
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an
tag
e
o
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e
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y
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m
etr
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an
d
th
er
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o
r
e
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eq
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ir
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a
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ig
n
al
le
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s
o
m
e
p
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wer
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r
th
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tr
an
s
f
o
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m
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r
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ir
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if
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e
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ig
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al
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s
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n
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ir
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2
(
N)
,
w
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ich
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ig
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if
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tly
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n
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ea
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es
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s
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g
s
p
ee
d
.
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h
e
n
u
m
b
er
o
f
s
am
p
les
in
th
e
o
r
ig
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al
wav
ef
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r
m
d
ir
ec
tly
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r
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ates
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e
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m
b
er
o
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d
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cr
ete
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e
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at
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alu
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as
p
ar
t
o
f
a
Fo
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r
ier
tr
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s
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o
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m
.
T
h
e
n
u
m
b
er
o
f
f
r
eq
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en
c
y
lin
es
o
r
b
in
s
is
eq
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al
to
N/2
,
wh
er
e
N
is
th
e
s
ig
n
al
len
g
th
.
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I
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Vo
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14
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4
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Dec
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20
25
:
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874
I
n
th
is
p
r
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an
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is
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s
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ally
h
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e
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v
ar
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o
f
f
r
eq
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e
n
cy
co
m
p
o
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en
ts
in
ad
d
itio
n
to
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d
m
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h
an
ical
n
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e.
T
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ata
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s
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s
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X
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NUCLE
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en
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r
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f
r
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F
ig
u
r
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3
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p
lete
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itu
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er
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s
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o
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tim
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Fig
u
r
e
3
.
Vib
r
ati
o
n
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al
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is
f
o
r
m
o
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r
f
au
lt d
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3
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2
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P
r
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po
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pp
ro
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ch
f
o
r
predict
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e
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ly
t
ics
Neu
r
al
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s
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ate,
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o
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n
d
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eq
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ir
e
h
u
n
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r
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d
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o
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th
o
u
s
an
d
s
o
f
tr
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in
g
cy
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wh
ich
ca
n
q
u
ick
l
y
lead
to
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ca
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m
in
i
m
u
m
s
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o
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e
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p
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p
r
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s
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u
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4
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ates a
g
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ith
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ir
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s
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ated
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Fig
u
r
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5
.
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h
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in
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er
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r
m
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r
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s
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s
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r
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r
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Fig
u
r
e
4
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le
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ith
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RE
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NC
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S
[
1
]
M
.
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