IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 53 - 86
Published: Feb. 5, 2025
Artificial
Intelligence
(AI)
and
Machine
Learning
(ML)
are
rapidly
changing
the
face
of
Research
Development
(R&D).
This
chapter
deals
with
a
profound
review
current
status
future
trends
AI
ML
in
R&D.
First
all,
it
gives
an
overview
huge
investments
fast
growth
AI,
for
instance,
spending
on
systems
worldwide
is
projected
to
reach
as
high
$110
billion
by
2024.
In
health
sector,
will
potentially
add
up
$150
every
year
2026.
The
highlights
some
most
remarkable
achievements
ML,
including
transformer
models
like
GPT-3
or
Google's
BERT,
setting
new
benchmarks
natural
language
processing,
low-code/no-code
platforms
democratize
AI.
Finally,
asserts
that
have
potential
transform
R&D
while
insinuating
such
development
should
be
responsible
ethical.
adopting
collaborative
open
approaches,
stakeholders
could
reap
maximum
benefits
from
technologies
boosting
innovation
societal
across
different
industries.
Results in Engineering,
Journal Year:
2024,
Volume and Issue:
22, P. 102132 - 102132
Published: April 21, 2024
Multiple
industries
have
been
revolutionized
by
the
incorporation
of
data
science
advancements
into
intelligent
environment
technologies,
specifically
in
context
smart
grids.
Smart
grids
offer
a
dynamic
and
efficient
framework
for
management
optimization
electricity
generation,
distribution,
consumption,
thanks
to
developments
big
analytics.
This
review
delves
integration
Grid
applications
Big
Data
analytics
reviewing
25
papers
screened
with
PRISMA
standard.
The
paper
matter
encompasses
critical
domains
including
adaptive
energy
management,
canonical
correlation
analysis,
novel
methodologies
blockchain
machine
learning.
emphasizes
contributions
efficiency,
security,
sustainability
means
rigorous
methodology.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(2), P. 854 - 854
Published: Jan. 16, 2025
This
paper
proposes
a
sustainable
model
for
integrating
robotic
process
automation
(RPA)
and
machine
learning
(ML)
in
predictive
maintenance
to
enhance
operational
efficiency
failure
prediction
accuracy.
The
research
identified
key
gap
the
literature,
namely
limited
integration
of
RPA,
ML,
sustainability
manufacturing,
which
led
development
this
model.
Using
PICO
methodology
(Population,
Intervention,
Comparison,
Outcome),
study
evaluated
implementation
these
technologies
Alpha
Company,
comparing
results
before
after
model’s
adoption.
intervention
integrated
RPA
ML
improve
accuracy
optimize
operations.
Results
showed
100%
increase
mean
time
between
failures
(MTBF),
67%
reduction
repair
(MTTR),
37.5%
decrease
costs,
71.4%
unplanned
downtime
costs.
Challenges
such
as
initial
costs
need
continuous
training
were
also
noted.
Future
could
explore
big
data
AI
further
demonstrates
that
leads
improvements,
cost
reductions,
environmental
benefits,
contributing
industrial
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.
Electronics,
Journal Year:
2025,
Volume and Issue:
14(5), P. 1010 - 1010
Published: March 3, 2025
Steam
traps
are
essential
for
industrial
systems,
ensuring
steam
quality
and
energy
efficiency
by
removing
condensate
preventing
leakage.
However,
their
failure
results
in
loss,
operational
disruptions,
increased
greenhouse
gas
emissions.
This
paper
proposes
a
novel
predictive
maintenance
system
that
integrates
statistical
time
series
features
transformer
encoder–decoder
models
fault
diagnosis
visualization.
The
proposed
combines
IoT
sensor
data,
parameters,
open
data
(e.g.,
weather
information
public
holiday
calendars),
machine
learning,
two-dimensional
diagnostic
projection
to
improve
reliability
interpretability.
Experiments
were
conducted
two
plants:
an
aluminum
processing
plant
food
manufacturing
plant,
the
achieved
superior
defect
detection
accuracy
compared
existing
methods.
transformer-based
model
outperformed
traditional
methods,
including
random
forest,
gradient
boosting,
variational
autoencoder,
classification
clustering.
also
demonstrated
average
6.92%
reduction
thermal
across
both
sites,
highlighting
its
potential
reduce
carbon
research
highlights
transformative
impact
of
AI-based
technologies
operations
provides
framework
sustainable
practices.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(6), P. 3166 - 3166
Published: March 14, 2025
Generative
AI
(GenAI)
is
revolutionizing
digital
twins
(DTs)
for
fault
diagnosis
and
predictive
maintenance
in
Industry
4.0
5.0
by
enabling
real-time
simulation,
data
augmentation,
improved
anomaly
detection.
DTs,
virtual
replicas
of
physical
systems,
already
use
generative
models
to
simulate
various
failure
scenarios
rare
events,
improving
system
resilience
prediction
accuracy.
They
create
synthetic
datasets
that
improve
training
quality
while
addressing
scarcity
imbalance.
The
aim
this
paper
was
present
the
current
state
art
perspectives
using
AI-based
DTs
4.0/5.0.
With
GenAI,
enable
proactive
minimize
downtime,
their
latest
implementations
combine
multimodal
sensor
generate
more
realistic
actionable
insights
into
performance.
This
provides
operational
profiles,
identifying
potential
traditional
methods
may
miss.
New
area
include
incorporation
Explainable
(XAI)
increase
transparency
decision-making
reliability
key
industries
such
as
manufacturing,
energy,
healthcare.
As
emphasizes
a
human-centric
approach,
DT
can
seamlessly
integrate
with
human
operators
support
collaboration
decision-making.
implementation
edge
computing
increases
scalability
capabilities
smart
factories
industrial
Internet
Things
(IoT)
systems.
Future
advances
federated
learning
ensure
privacy
exchange
between
enterprises
diagnostics,
evolution
GenAI
alongside
ensuring
long-term
validity.
However,
challenges
remain
managing
computational
complexity,
security,
ethical
issues
during
implementation.
Journal of Artificial Intelligence and Big Data,
Journal Year:
2024,
Volume and Issue:
4(1), P. 48 - 60
Published: Feb. 15, 2024
Failure
prediction
can
be
achieved
through
prognostics,
which
provides
timely
warnings
before
failure.
is
crucial
in
an
effective
prognostic
system,
allowing
preventive
maintenance
actions
to
avoid
downtime.
The
prognostics
problem
involves
estimating
the
remaining
useful
life
(RUL)
of
a
system
or
component
at
any
given
time.
RUL
defined
as
time
from
current
goal
make
accurate
predictions
close
failure
provide
early
warnings.
J
S
Grewal
and
J.
comprehensive
definition
their
paper
"The
Kalman
Filter
approach
estimation."
A
process
quadruple
(XU
f
P),
where
X
state
space,
U
control
P
set
possible
paths,
represents
transition
between
states.
applying
values
change
system's
over
