Sensors,
Journal Year:
2024,
Volume and Issue:
25(1), P. 180 - 180
Published: Dec. 31, 2024
The
core
of
this
publication
is
the
design
a
system
for
evaluating
condition
production
equipment
and
machines
by
monitoring
selected
parameters
process
with
an
additional
sensor
subsystem.
main
positive
processing
data
from
layer
using
artificial
intelligence
(AI)
expert
systems
(ESs)
use
edge
computing
(EC).
Sensor
information
processed
directly
at
level
on
monitored
equipment,
results
individual
subsystems
are
stored
in
form
triggers
database
predictive
maintenance
process.
whole
solution
includes
suitable
sensors
implementation
layer,
description
algorithms,
communication
infrastructure
system,
tests
experimental
operation
device
laboratory
conditions.
visualisation
status
operator
interactive
online
map.
Infrastructures,
Journal Year:
2024,
Volume and Issue:
9(12), P. 225 - 225
Published: Dec. 7, 2024
This
study
explores
the
growing
influence
of
artificial
intelligence
(AI)
on
structural
health
monitoring
(SHM),
a
critical
aspect
infrastructure
maintenance
and
safety.
begins
with
bibliometric
analysis
to
identify
current
research
trends,
key
contributing
countries,
emerging
topics
in
AI-integrated
SHM.
We
examine
seven
core
areas
where
AI
significantly
advances
SHM
capabilities:
(1)
data
acquisition
sensor
networks,
highlighting
improvements
technology
collection;
(2)
processing
signal
analysis,
techniques
enhance
feature
extraction
noise
reduction;
(3)
anomaly
detection
damage
identification
using
machine
learning
(ML)
deep
(DL)
for
precise
diagnostics;
(4)
predictive
maintenance,
optimize
scheduling
prevent
failures;
(5)
reliability
risk
assessment,
integrating
diverse
datasets
real-time
analysis;
(6)
visual
inspection
remote
monitoring,
showcasing
role
AI-powered
drones
imaging
systems;
(7)
resilient
adaptive
infrastructure,
enables
systems
respond
dynamically
changing
conditions.
review
also
addresses
ethical
considerations
societal
impacts
SHM,
such
as
privacy,
equity,
transparency.
conclude
by
discussing
future
directions
challenges,
emphasizing
potential
efficiency,
safety,
sustainability
systems.
Machines,
Journal Year:
2025,
Volume and Issue:
13(1), P. 36 - 36
Published: Jan. 7, 2025
The
transition
from
Industry
4.0
to
5.0
gives
more
prominence
human-centered
and
sustainable
manufacturing
practices.
This
paper
proposes
a
conceptual
design
framework
based
on
Vision
Transformers
(ViTs)
digital
twins,
meet
the
demands
of
5.0.
ViTs,
known
for
their
advanced
visual
data
analysis
capabilities,
complement
simulation
optimization
capabilities
which
in
turn
can
enhance
predictive
maintenance,
quality
control,
human–machine
symbiosis.
applied
is
capable
analyzing
multidimensional
data,
integrating
operational
streams
real-time
tracking
application
decision
making.
Its
main
characteristics
are
anomaly
detection,
analytics,
adaptive
optimization,
line
with
objectives
sustainability,
resilience,
personalization.
Use
cases,
including
maintenance
demonstrate
higher
efficiency,
waste
reduction,
reliable
operator
interaction.
In
this
work,
emergent
role
ViTs
twins
development
intelligent,
dynamic,
human-centric
industrial
ecosystems
discussed.
Journal of Sensor and Actuator Networks,
Journal Year:
2025,
Volume and Issue:
14(1), P. 16 - 16
Published: Feb. 4, 2025
This
paper
proposes
a
pathway
for
smart
maintenance
by
addressing
overarching
questions
and
key
impediments
that
arise
when
manufacturing
companies
are
exploring
investments
in
such
projects.
The
proposed
consists
of
seven
distinct
steps
at
which
analytical
models
used
to
predict
the
impact
on
system-level
operational
performance
indicators
(KPIs)
resulting
return
investment
(ROI).
advantage
this
approach
is
rely
few
parameters
and,
therefore,
can
be
even
there
no
sophisticated
data
collection
systems
place,
as
case
many
small
medium
enterprises
(SMEs).
Furthermore,
allows
development
“personalized”
along
with
prediction
improvement
ROI
impact,
enabling
management
make
decisions
greater
confidence.
also
three-step
detour
unprepared
embark
their
journey
towards
maintenance.
application
illustrated
through
studies
consisting
three
real
SMEs.
First,
maintenance,
we
suggest
traditional
variance
reduction
methods
appropriate
goals
predicted
improvements
financial
KPIs.
Next,
prepared
provide
detailed
evaluation
condition-based
(CBM)
analyzing
various
machine
combinations
maximize
performance-to-cost
ratio.
In
one
SME,
our
analysis
shows
an
throughput
(0
3%)
(26:1)
achievable
adoption
visualized
using
DuPont
Model.
International Journal of Scientific Research in Computer Science Engineering and Information Technology,
Journal Year:
2025,
Volume and Issue:
11(1), P. 2246 - 2256
Published: Feb. 10, 2025
The
integration
of
Artificial
Intelligence
with
Manufacturing
Execution
Systems
is
revolutionizing
the
industrial
landscape,
ushering
in
a
new
era
smart
manufacturing.
This
comprehensive
article
explores
how
AI-enhanced
MES
transforms
traditional
manufacturing
operations
through
advanced
predictive
maintenance,
intelligent
scheduling,
and
automated
quality
control.
implementation
challenges,
including
data
change
management,
while
highlighting
successful
case
studies
factory
transformation.
By
exploring
convergence
AI
MES,
demonstrates
facilities
achieve
significant
improvements
operational
efficiency,
control,
resource
utilization.
also
future
directions,
edge
computing
integration,
digital
twin
technologies,
cross-plant
optimization,
providing
valuable
insights
for
organizations
planning
their
transformation
journey.
International Journal of Inventive Engineering and Sciences,
Journal Year:
2025,
Volume and Issue:
12(2), P. 18 - 26
Published: Feb. 20, 2025
The
reliability
of
critical
assets
is
essential
for
operational
success
and
long-term
sustainability
in
modern
manufacturing.
Asset
Integrity
Management
(AIM)
ensures
reliability,
availability,
maintainability,
safety
(RAMS)
while
minimizing
risks
costs.
Industry
4.0
technologies—such
as
the
Internet
Things
(IoT),
Artificial
Intelligence
(AI),
Big
Data
analytics—have
revolutionized
maintenance
strategies,
enabling
real-time
monitoring,
predictive
diagnostics,
data-driven
decision-making.
These
advancements
have
transformed
AIM,
optimizing
asset
performance
efficiency.
Maintenance
leverages
these
technologies
to
integrate
preventive
maintenance,
proactive
repairs,
reducing
costly
failures,
enhancing
equipment
productivity.
This
paper
examines
impact
on
focusing
transition
from
reactive
intelligent,
technology-driven
solutions.
It
highlights
benefits
improved
efficiency,
optimized
schedules,
cost
reduction,
risk
mitigation,
competitive
manufacturing
sector.
Through
a
comprehensive
literature
review,
this
study
identifies
gaps
aligning
traditional
practices
with
emerging
proposes
framework
address
challenges.
By
combining
advanced
digital
established
AIM
principles,
research
offers
strategic
roadmap
integrity,
achieving
excellence,
fostering
sustainable
growth
