Advances in Mechanical Engineering,
Journal Year:
2024,
Volume and Issue:
16(12)
Published: Dec. 1, 2024
With
the
continuous
development
of
economy
and
society,
factors
such
as
variety
underwater
targets
high
level
environmental
noise
have
a
great
impact
on
classification
accuracy
target
radiation
noise,
traditional
method
based
signal
features
can
no
longer
meet
requirements
identification.
In
this
paper,
we
propose
an
enhanced
image
convolutional
neural
network.
First,
is
converted
into
by
various
methods,
then
data
set
used
input
network
for
model
training,
finally
advantage
in
to
accurately
classify
noise.
order
optimal
augmented
transformation
method,
paper
uses
several
methods
compares
results.
The
experimental
results
show
that
lagomorphs
corner
fields
highest
best
efficiency.
Sensors,
Journal Year:
2024,
Volume and Issue:
24(5), P. 1680 - 1680
Published: March 5, 2024
This
study
introduces
a
novel
nonlinear
dynamic
analysis
method,
known
as
beluga
whale
optimization–slope
entropy
(BWO-SlEn),
to
address
the
challenge
of
recognizing
sea
state
signals
(SSSs)
in
complex
marine
environments.
A
method
underwater
acoustic
signal
recognition
based
on
BWO-SlEn
and
one-dimensional
convolutional
neural
network
(1D-CNN)
is
proposed.
Firstly,
particle
swarm
(PSO-SlEn),
BWO-SlEn,
Harris
hawk
(HHO-SlEn)
were
used
for
feature
extraction
noise
SSS.
After
1D-CNN
classification,
found
have
best
effect.
Secondly,
fuzzy
(FE),
sample
(SE),
permutation
(PE),
dispersion
(DE)
extract
features.
highest
rate
compared
with
them.
Finally,
other
six
methods,
rates
SSS
are
at
least
6%
4.75%
higher,
respectively.
Therefore,
methods
proposed
this
paper
more
effective
application
recognition.
Nondestructive Testing And Evaluation,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 19
Published: April 4, 2024
Rolling
bearings
are
widely
used
in
rotating
machinery,
such
as
aero-engine
spindles,
flying
machines,
wind
turbines,
etc.
Bearing
condition
monitoring
is
of
practical
importance.
The
acoustic
emission
(AE)
signal
has
impact
and
rapid
attenuation
characteristics.
Most
existing
research
on
fault
diagnosis
not
focused
According
to
this
characteristic,
a
time-frequency
coherent
energy
change
rate
(TFC-TFECR)
method
proposed
identify
the
AE
signals
bearing
faults.
This
paper
investigates
effect
(TFC)
coefficient.
It
also
focuses
deviation
TFC-TFECR
method,
which
superior
energy.
Feature
extraction
from
cylindrical
roller
carried
out
through
three
typical
states
bearings.
feature
values
input
into
SVM
model,
sparrow
search
algorithm
optimises
model.
experimental
results
show
that
can
effectively
realise
state
recognition
bearings,
accuracy
reaches
99.3827%
at
600
r/min
98.7654%
1200
r/min.
provides
new
for
non-destructive
testing
machinery
Nondestructive Testing And Evaluation,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 20
Published: June 13, 2024
Ensuring
the
safety
of
pipeline
transportation
is
vital
for
societal
well-being.
Traditional
methods
identifying
defects
in
steel
pipelines
through
ultrasonic
echo
signal
pattern
recognition
often
fail
to
extract
comprehensive
and
effective
features
crucial
accurate
defect
detection.
This
study
introduces
an
innovative
feature
extraction
method
employing
a
fractional
Fourier
transform
variational
modal
decomposition
(FRFT-VMD),
hereafter
referred
as
fractional-order
VMD
algorithm.
utilises
fourth-order
central
moment
envelope
entropy
optimise
several
key
parameters:
order
transform,
number
layers,
penalty
factor
decomposition.
To
evaluate
effectiveness
proposed
method,
signals
from
both
finite
element
simulations
experimental
platforms
were
analysed
using
FRFT-VMD
technique.
The
extracted
then
classified
Least
Squares
Support
Vector
Machine
(LSSVM)
determine
depths.
results
show
accuracy
95.2%
simulated
89.1%
experimentally
measured
across
various
depths,
indicating
significant
improvement
over
existing
methodologies.
algorithm
proves
be
superior
extracting
that
enhance
identification
pipelines.
