Engineering Research Express,
Journal Year:
2024,
Volume and Issue:
6(4), P. 045205 - 045205
Published: Sept. 24, 2024
Abstract
To
solve
the
problem
of
difficulty
in
extracting
and
identifying
fault
types
during
turbine
rotor
operation,
a
diagnosis
method
based
on
improved
subtraction
mean
optimizer
(NGSABO)
algorithm
to
optimize
variational
mode
decomposition
(VMD)
CNN-BiLSTM
neural
network
is
proposed.
Firstly,
three
improvements
are
made
average
algorithm.
Secondly,
optimal
VMD
parameter
combination
NGSABO
adaptive
selection
number
K
penalty
factor
α
used
decompose
signal,
minimum
sample
entropy
as
fitness
function
for
feature
extraction.
Combining
convolutional
bidirectional
long
short-term
memory
identify
classify
features.
Compared
with
other
methods,
this
has
outstanding
performance
single
coupled
faults.
The
accuracy
reaches
98.5714%,
which
good
practical
application
value.
Physics of Fluids,
Journal Year:
2025,
Volume and Issue:
37(2)
Published: Feb. 1, 2025
GH4099
is
a
typical
age-hardened
nickel-based
superalloy
with
excellent
overall
performance,
widely
used
in
aerospace
and
other
fields.
In
this
study,
novel
tight-coupled
dual-gas
nozzle
designed,
two-phase
coupling
breakup
model
for
the
atomization
process
established
based
on
volume
of
fluid
flow
model.
The
behavior
melt
under
high-speed
gas
investigated
depth.
generation
droplets
analyzed,
nozzle,
enters
chamber
first
impacted
by
intermediate
airflow
to
generate
initial
droplets,
move
toward
outer
air
channel
action
continue
break
into
smaller
channel.
Powder
particles
are
sampled
at
exit,
particle
characteristics
generated
analyzed
detail.
final
size
distribution
obtained,
influence
pressure
injection
angle
explored.
results
show
that,
within
studied
parameter
range,
as
increases,
powder
increases
then
decreases.
As
decreases,
also
so
small
favorable
reduction.
When
P2
=
4.5
MPa,
α
25°,
has
narrowest
distribution,
smaller,
median
diameter
D50
29.1
μm.
findings
study
provide
important
references
structure
design
optimization
high-temperature
alloys.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(22), P. 10404 - 10404
Published: Nov. 12, 2024
This
paper
presents
a
fault
diagnosis
technique
for
milling
machines
based
on
acoustic
emission
(AE)
signals
and
hybrid
deep
learning
model
optimized
with
genetic
algorithm.
Mechanical
failures
in
machines,
particularly
critical
components
like
cutting
tools,
gears,
bearings,
account
significant
portion
of
operational
breakdowns,
leading
to
unplanned
downtime
financial
losses.
To
address
this
issue,
the
proposed
method
first
acquires
AE
from
machine.
signals,
capturing
dynamic
responses
machine
components,
are
transformed
into
continuous
wavelet
transform
(CWT)
scalograms
further
analysis.
Gaussian
filtering
is
applied
enhance
clarity
these
scalograms,
effectively
reducing
noise
while
maintaining
essential
features.
A
convolutional
neural
network
(CNN)
VGG16
architecture
utilized
spatial
feature
extraction,
followed
by
bidirectional
long
short-term
memory
(BiLSTM)
capture
temporal
dependencies
scalograms.
The
algorithm
(GA)
used
optimize
selection
ensure
most
relevant
features
improve
model’s
performance.
finally
fed
fully
connected
(FC)
layer
classification.
achieves
an
accuracy
99.6%,
significantly
outperforming
traditional
approaches.
offers
highly
accurate
efficient
solution
detection
allowing
more
reliable
predictive
maintenance
efficiency
industrial
settings.
