International Journal of Electrochemical Science,
Journal Year:
2024,
Volume and Issue:
unknown, P. 100686 - 100686
Published: June 1, 2024
The
transportation
of
oil
and
gas
relies
heavily
on
pipelines,
pipeline
corrosion
is
a
major
factor
affecting
reliability.
It
can
lead
to
failure
other
damage.
Pipeline
prediction
great
importance
for
integrity
management
prevention.
A
physical
law
intervening
RF(Random
Forest)-PSO(Particle
Swarm
Optimization)-BP(Back
Propagation
Neural
Network)
algorithm
proposed
predict
rate.
DeWaard
model
first
fitted
the
data,
predicts
form
new
feature,
which
then
combined
with
features
extracted
by
RF
feature
that
used
as
an
input
metric
data-driven
model.
Secondly,
already
constructed
are
divided
into
training
set
testing
set.
train
PSO-BP
model,
test
accuracy
evaluated
using
metrics
such
MAE,
MBE,
MAPE,
R2.
To
show
superiority
compared
models.
results
has
some
advantages
in
both
analysis
prediction,
it
theoretical
guidance
protection.
Mathematics,
Journal Year:
2023,
Volume and Issue:
11(18), P. 4013 - 4013
Published: Sept. 21, 2023
A
novel
method
is
proposed
to
investigate
the
pattern
of
variation
in
residual
strength
and
reliability
wind
turbine
gear.
First,
interaction
between
loads
effect
loading
sequence
considered
based
on
fatigue
damage
accumulation
theory,
a
degradation
model
with
few
parameters
established.
Experimental
data
from
two
materials
are
used
verify
predictive
performance
model.
Secondly,
modeling
simulation
gear
conducted
analyze
types
failures
obtain
their
life
curves.
Due
randomness
load
gear,
rain
flow
counting
Goodman
employed.
Thirdly,
considering
seasonal
load,
decreasing
trend
under
multistage
random
calculated.
Finally,
dynamic
failure
rate
analyzed.
The
results
demonstrate
that
increases
increasing
service
time.
seasonality
causes
fluctuations
providing
new
idea
for
evaluating
International Journal of Electrochemical Science,
Journal Year:
2024,
Volume and Issue:
unknown, P. 100686 - 100686
Published: June 1, 2024
The
transportation
of
oil
and
gas
relies
heavily
on
pipelines,
pipeline
corrosion
is
a
major
factor
affecting
reliability.
It
can
lead
to
failure
other
damage.
Pipeline
prediction
great
importance
for
integrity
management
prevention.
A
physical
law
intervening
RF(Random
Forest)-PSO(Particle
Swarm
Optimization)-BP(Back
Propagation
Neural
Network)
algorithm
proposed
predict
rate.
DeWaard
model
first
fitted
the
data,
predicts
form
new
feature,
which
then
combined
with
features
extracted
by
RF
feature
that
used
as
an
input
metric
data-driven
model.
Secondly,
already
constructed
are
divided
into
training
set
testing
set.
train
PSO-BP
model,
test
accuracy
evaluated
using
metrics
such
MAE,
MBE,
MAPE,
R2.
To
show
superiority
compared
models.
results
has
some
advantages
in
both
analysis
prediction,
it
theoretical
guidance
protection.
