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.
Engineering Science & Technology Journal,
Journal Year:
2024,
Volume and Issue:
5(6), P. 2039 - 2049
Published: June 13, 2024
This
review
paper
explores
the
policy
requirements,
implementation
strategies,
challenges,
and
future
directions
of
digital
twin
technology
in
oil
gas
industry.
It
discusses
regulatory
framework,
data
governance,
compliance,
safety,
intellectual
property
considerations
essential
for
successful
integration.
Implementation
strategies
encompass
strategic
planning,
technological
integration,
skills
development,
change
management.
Challenges
such
as
accuracy,
interoperability,
cost
implications,
ethical
concerns
are
analysed.
Future
trends,
including
advanced
analytics,
edge
computing,
IoT
developing
a
ecosystem,
discussed.
By
addressing
these
aspects,
organisations
can
leverage
to
enhance
efficiency,
sustainability
operations.
Keywords:
Digital
Twin
Technology,
Oil
And
Gas
Industry,
Policy
Requirements,
Strategies,
Data,
Journal Year:
2024,
Volume and Issue:
9(5), P. 69 - 69
Published: May 15, 2024
Vibration-based
condition
monitoring
plays
an
important
role
in
maintaining
reliable
and
effective
heavy
machinery
various
sectors.
Heavy
involves
major
investments
is
frequently
subjected
to
extreme
operating
conditions.
Therefore,
prompt
fault
identification
preventive
maintenance
are
for
reducing
costly
breakdowns
operational
safety.
In
this
review,
we
look
at
different
methods
of
vibration
data
processing
the
context
vibration-based
machinery.
We
divided
primary
approaches
related
into
three
categories–signal
methods,
preprocessing-based
techniques
artificial
intelligence-based
methods.
highlight
importance
these
improving
reliability
effectiveness
systems,
highlighting
precise
automated
detection
systems.
To
improve
performance
efficiency,
review
aims
provide
information
on
current
developments
future
directions
by
addressing
issues
like
imbalanced
integrating
cutting-edge
anomaly
algorithms.
Applied Mechanics,
Journal Year:
2025,
Volume and Issue:
6(1), P. 8 - 8
Published: Jan. 31, 2025
Experimentable
Digital
Twins
are
capable
of
combining
different
simulation
domains
on
a
system
level.
This
has
been
shown
for
multitude
domains,
e.g.,
rigid
body
dynamics,
control,
sensors,
kinematics,
etc.,
and
application
scenarios,
automotive,
space,
industrial
engineering.
In
our
work,
we
investigate
how
to
include
structural
loads
into
an
Twin
while
maintaining
computational
efficiency
interoperability
We
combine
dynamics
with
the
transfer
matrix
method
simulate
forces
stresses.
show
approach
statically
determinate
beam
structures
in
level
validate
it
experimentally
numerically
static
dynamic
example
problems.
The
results
strong
agreement
these
comparisons,
confirming
accuracy
reliability
method.
For
practical
applications,
see
force
stress
using
as
additional
tool
facilitate
simulation-based
engineering
early
stages
design
processes,
when
dealing
uncertain
loading
conditions
operational
complexity
Applied Mathematics and Nonlinear Sciences,
Journal Year:
2025,
Volume and Issue:
10(1)
Published: Jan. 1, 2025
Abstract
Ensuring
reliable
oil
and
gas
transport
through
pipelines
remains
a
core
engineering
challenge,
particularly
in
the
face
of
expanding
infrastructure
complex
operating
conditions.
Conventional
approaches
often
lack
real-time
insight
predictive
capabilities
required
for
timely
anomaly
detection
effective
maintenance
scheduling.
In
this
paper,
we
propose
digital
twin-based
solution
that
integrates
physics-driven
fluid
structural
modeling
with
an
Ensemble
Kalman
Filter
(EnKF)
data
assimilation.
Our
framework
continuously
updates
pipeline
states
based
on
multi-sensor
feedback
applies
machine
learning
module
to
classify
anomalies
such
as
leaks,
blockages,
corrosion.
Through
synergy
physical
simulations
data-driven
analytics,
early
faults
are
identified
accurately,
decisions
generated
reduce
operational
costs
prevent
catastrophic
failures.
Experimental
evaluations
multiple
scenarios
demonstrate
improved
precision
robustness,
indicating
significant
potential
twin
technology
proactive
intelligent
management.
