Machines,
Journal Year:
2024,
Volume and Issue:
12(12), P. 921 - 921
Published: Dec. 16, 2024
The
rotating
pump
of
pipelines
are
susceptible
to
damage
based
on
extended
operations
in
a
complex
environment
high
temperature
and
pressure,
which
leads
abnormal
vibrations
noises.
Currently,
the
method
for
detecting
conditions
pumps
primarily
involves
identifying
their
sounds
vibrations.
Due
background
noise,
performance
condition
monitoring
is
unsatisfactory.
To
overcome
this
issue,
pipeline
proposed
by
extracting
fusing
sound
vibration
features
different
ways.
Firstly,
hand-crafted
feature
set
established
from
two
aspects
vibration.
Moreover,
convolutional
neural
network
(CNN)-derived
one-dimensional
CNN
(1D
CNN).
For
CNN-derived
sets,
selection
presented
significant
ranking
according
importance,
calculated
ReliefF
random
forest
score.
Finally,
applied
at
level.
According
signals
obtained
experimental
platform,
was
evaluated,
showing
an
average
accuracy
93.27%
conditions.
effectiveness
superiority
manifested
through
comparison
ablation
experiments.
Nondestructive Testing And Evaluation,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 31
Published: May 4, 2024
Guided
ultrasonic
wave
(GUW)
monitoring
systems
for
pipeline
structures
are
gaining
much
attention
in
critical
sectors
such
as
the
petrochemical,
nuclear
and
energy
sectors.
However,
effects
of
environmental
operational
conditions
(EOCs),
especially
temperature,
may
generate
substantial
false
damage
detections.
The
temperature
effect
interfere
with
different
coherent
noise
sources
unwanted
peaks
that
falsely
identified
damage.
In
this
paper,
a
denoising
autoencoder
(DAE)
is
proposed
to
reduce
frequency
detections
GUW
systems.
A
DAE
decodes
high
dimensional
data
into
low-dimensional
features
reconstructs
original
from
these
features.
By
providing
signals
at
reference
fewest
detections,
structure
forces
learn
essential
hidden
within
complex
data.
database
formed
based
on
experimental
measurements
using
six-metre-long
stainless
steel
Schedule
20
pipe.
Variations
severity
applied
develop
mimic
simple
step
change
growth
under
EOCs.
outcomes
obtained
study
show
methodology
can
during
valuable
safety
evaluations.
Machines,
Journal Year:
2024,
Volume and Issue:
12(12), P. 921 - 921
Published: Dec. 16, 2024
The
rotating
pump
of
pipelines
are
susceptible
to
damage
based
on
extended
operations
in
a
complex
environment
high
temperature
and
pressure,
which
leads
abnormal
vibrations
noises.
Currently,
the
method
for
detecting
conditions
pumps
primarily
involves
identifying
their
sounds
vibrations.
Due
background
noise,
performance
condition
monitoring
is
unsatisfactory.
To
overcome
this
issue,
pipeline
proposed
by
extracting
fusing
sound
vibration
features
different
ways.
Firstly,
hand-crafted
feature
set
established
from
two
aspects
vibration.
Moreover,
convolutional
neural
network
(CNN)-derived
one-dimensional
CNN
(1D
CNN).
For
CNN-derived
sets,
selection
presented
significant
ranking
according
importance,
calculated
ReliefF
random
forest
score.
Finally,
applied
at
level.
According
signals
obtained
experimental
platform,
was
evaluated,
showing
an
average
accuracy
93.27%
conditions.
effectiveness
superiority
manifested
through
comparison
ablation
experiments.
