Applied Sciences,
Journal Year:
2023,
Volume and Issue:
13(3), P. 1790 - 1790
Published: Jan. 30, 2023
In
an
increasingly
competitive
industrial
world,
the
need
to
adapt
any
change
at
time
has
become
a
major
necessity
for
every
industry
remain
and
survive
in
their
environments.
Industries
are
undergoing
rapid
perpetual
changes
on
several
levels.
Indeed,
latter
requires
companies
be
more
reactive
involved
policies
of
continuous
improvement
order
satisfy
customers
maximize
quantity
quality
production,
while
keeping
cost
production
as
low
possible.
Reducing
downtime
is
one
objectives
these
industries
future.
This
paper
aimed
apply
machine
learning
algorithms
TA-48
multistage
centrifugal
compressor
failure
prediction
remaining
useful
life
(RUL),
i.e.,
reduce
system
using
predictive
maintenance
(PdM)
approach
through
adoption
Industry
4.0
approaches.
To
achieve
our
goal,
we
followed
methodology
workflow
that
allows
us
explore
process
data
model
training.
Thus,
comparative
study
different
was
carried
out
arrive
final
choice,
which
based
implementation
LSTM
neural
networks.
addition,
its
performance
improved
sets
were
fed
incremented.
Finally,
deployed
allow
operators
know
times
compressors
subsequently
ensure
minimum
rates
by
making
decisions
before
failures
occur.
Discover Artificial Intelligence,
Journal Year:
2023,
Volume and Issue:
3(1)
Published: Dec. 7, 2023
Abstract
Driven
by
the
ongoing
migration
towards
Industry
4.0,
increasing
adoption
of
artificial
intelligence
(AI)
has
empowered
smart
manufacturing
and
digital
transformation.
AI
enhances
industry
4.0
through
AI-based
decision-making
analyzing
real-time
data
to
optimize
different
processes
such
as
production
planning,
predictive
maintenance,
quality
control
etc.,
thus
guaranteeing
reduced
costs,
high
precision,
efficiency
accuracy.
This
paper
explores
AI-driven
manufacturing,
revolutionizing
traditional
approaches
unlocking
new
possibilities
throughout
major
phases
industrial
equipment
lifecycle.
Through
a
comprehensive
review,
we
delve
into
wide
range
techniques
employed
tackle
challenges
optimizing
process
control,
machining
parameters,
facilitating
decision-making,
elevating
maintenance
strategies
within
an
These
encompass
design,
recycling/retrofitting.
As
reported
in
2022
McKinsey
Global
Survey
(
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review
),
witnessed
more
than
two-fold
increase
since
2017.
contributed
research
last
six
years.
Therefore,
from
meticulous
search
relevant
electronic
databases,
carefully
selected
synthesized
42
articles
spanning
01
January
2017
20
May
2023
highlight
review
most
recent
research,
adhering
specific
inclusion
exclusion
criteria,
shedding
light
on
latest
trends
popular
adopted
researchers.
includes
Convolutional
Neural
Networks
(CNN),
Generative
Adversarial
(GAN),
Bayesian
Networks,
Support
Vector
Machines
(SVM)
which
are
extensively
discussed
this
paper.
Additionally,
provide
insights
advantages
(e.g.,
enhanced
decision
making)
integration
with
legacy
systems
due
technical
complexities
compatibilities)
integrating
across
stages
operations.
Strategically
implementing
each
phase
enables
industries
achieve
productivity,
improved
product
quality,
cost-effectiveness,
sustainability.
exploration
potential
fosters
agile
resilient
processes,
keeping
at
forefront
technological
advancements
harnessing
full
solutions
improve
products.
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(2), P. 898 - 898
Published: Jan. 20, 2024
Predictive
maintenance
(PdM)
is
a
policy
applying
data
and
analytics
to
predict
when
one
of
the
components
in
real
system
has
been
destroyed,
some
anomalies
appear
so
that
can
be
performed
before
breakdown
takes
place.
Using
cutting-edge
technologies
like
artificial
intelligence
(AI)
enhances
performance
accuracy
predictive
systems
increases
their
autonomy
adaptability
complex
dynamic
working
environments.
This
paper
reviews
recent
developments
AI-based
PdM,
focusing
on
key
components,
trustworthiness,
future
trends.
The
state-of-the-art
(SOTA)
techniques,
challenges,
opportunities
associated
with
PdM
are
first
analyzed.
integration
AI
into
real-world
applications,
human–robot
interaction,
ethical
issues
emerging
from
using
AI,
testing
validation
abilities
developed
policies
later
discussed.
study
exhibits
potential
areas
for
research,
such
as
digital
twin,
metaverse,
generative
collaborative
robots
(cobots),
blockchain
technology,
trustworthy
Industrial
Internet
Things
(IIoT),
utilizing
comprehensive
survey
current
SOTA
opportunities,
challenges
allied
PdM.
Journal of Manufacturing Systems,
Journal Year:
2023,
Volume and Issue:
68, P. 376 - 399
Published: May 9, 2023
To
provide
direction
and
advice
for
future
research
on
Industry
4.0
maintenance,
we
conducted
a
comprehensive
analysis
of
344
eligible
journal
papers
published
between
2013
2022.
Our
systematic
literature
review
identifies
key
trends
in
advanced
maintenance
techniques
the
consolidation
traditional
concepts,
which
are
driven
by
increasing
adoption
technologies
need
to
optimize
manufacturing
systems'
performance
reliability.
In
light
our
findings,
highlight
importance
addressing
sustainability
factors,
human
aspects,
implementation
environmental
KPIs
research.
Building
upon
these
insights,
introduce
Maintenance
5.0
framework,
emphasizes
integration
human-centered
AI-driven
strategies
achieving
efficient
sustainable
Zero-Defect
Manufacturing
(ZDM)
systems.
We
propose
novel
framework
that
links
policies
small
medium-sized
enterprises
(SMEs)
facilitate
field.
This
work
underscores
bridge
gap
policies,
enabling
seamless
transition
SMEs
towards
practices,
while
fostering
socially
responsible
operations.
Computer Science & IT Research Journal,
Journal Year:
2024,
Volume and Issue:
5(5), P. 1090 - 1112
Published: May 5, 2024
The
oil
and
gas
industry
faces
significant
challenges
in
managing
equipment
maintenance
due
to
the
complexity
criticality
of
its
assets.
Traditional
approaches
are
often
reactive
inefficient,
leading
costly
downtime
safety
risks.
However,
emergence
artificial
intelligence
(AI)
predictive
technologies
offers
a
transformative
solution
these
challenges.
This
paper
explores
role
AI-driven
revolutionizing
management
sector.
leverages
machine
learning
algorithms
analyze
data
predict
when
is
required
before
breakdown
occurs.
By
monitoring
performance
real-time,
AI
can
identify
potential
issues
early,
allowing
operators
take
proactive
actions.
approach
helps
minimize
downtime,
reduce
costs,
improve
overall
reliability
safety.
implementation
requires
comprehensive
strategy
that
includes
collection,
analysis,
integration
with
existing
practices.
Successful
adoption
lead
benefits
for
companies,
including
increased
uptime,
extended
asset
lifespan,
enhanced
operational
efficiency.
reviews
current
landscape
industry,
highlighting
limitations
traditional
practices
need
more
approach.
It
then
examines
principles
maintenance,
showcasing
real-world
examples
successful
implementation.
Finally,
discusses
considerations
implementing
provides
recommendations
companies
looking
transform
their
Keywords:
Transforming
Equipment;
Management;
Oil
Gas;
AI-Driven;
Predictive
Maintenance.
Results in Engineering,
Journal Year:
2024,
Volume and Issue:
21, P. 101823 - 101823
Published: Jan. 27, 2024
The
progress
of
our
society
is
reflected
in
the
building
sector,
which
emphasises
necessity
constantly
modifying
instruments
to
take
advantage
new
opportunities.
An
example
cutting-edge
technology
with
potential
completely
transform
construction
sector
Internet
Things
(IoT).
goal
this
comprehensive
analysis
help
industry
improve
understanding
how
crucial
it
embrace
IoT.
In
study,
a
systematic
review
relevant
literature
was
conducted
identify
factors
that
contribute
enhancing
IoT
applications
industry.
primary
objective
list
and
evaluate
most
important
uses,
advantages
difficulties
using
sector.
This
revealed
has
significant
by
improving
productivity,
safety,
sustainability
quality
across
entire
lifecycle.
However,
barriers
such
as
data
privacy
cybersecurity
lack
standardised
protocols
need
be
addressed.
concludes
likely
revolutionise
coming
years
if
these
challenges
can
overcome.
These
findings
imply
firms
experiment
analytic
tools
phased
use
cases,
whilst
policy
groups
must
collaborate
on
standards
protocols.
Although
obstacles
exist,
strategic
implementation
promises
major
operational
breakthroughs
near
future.
Computers,
Journal Year:
2025,
Volume and Issue:
14(3), P. 93 - 93
Published: March 6, 2025
Machine
learning
(ML)
and
deep
(DL),
subsets
of
artificial
intelligence
(AI),
are
the
core
technologies
that
lead
significant
transformation
innovation
in
various
industries
by
integrating
AI-driven
solutions.
Understanding
ML
DL
is
essential
to
logically
analyse
applicability
identify
their
effectiveness
different
areas
like
healthcare,
finance,
agriculture,
manufacturing,
transportation.
consists
supervised,
unsupervised,
semi-supervised,
reinforcement
techniques.
On
other
hand,
DL,
a
subfield
ML,
comprising
neural
networks
(NNs),
can
deal
with
complicated
datasets
health,
autonomous
systems,
finance
industries.
This
study
presents
holistic
view
technologies,
analysing
algorithms
application’s
capacity
address
real-world
problems.
The
investigates
application
which
techniques
implemented.
Moreover,
highlights
latest
trends
possible
future
avenues
for
research
development
(R&D),
consist
developing
hybrid
models,
generative
AI,
incorporating
technologies.
aims
provide
comprehensive
on
serve
as
reference
guide
researchers,
industry
professionals,
practitioners,
policy
makers.
Journal of Marine Science and Engineering,
Journal Year:
2025,
Volume and Issue:
13(3), P. 425 - 425
Published: Feb. 25, 2025
The
maritime
industry
has
a
significant
influence
on
the
global
economy,
underscoring
need
for
operational
availability
and
safety
through
effective
maintenance
practices.
Predictive
emerges
as
promising
solution
compared
to
conventional
schemes
currently
employed
by
industry,
offering
proactive
failure
predictions,
reduced
downtime
events,
extended
machinery
lifespan.
This
paper
addresses
critical
gap
in
existing
literature
providing
comprehensive
overview
of
main
data-driven
PdM
systems.
Specifically,
review
explores
common
issues
found
vessel
components
(i.e.,
propulsion,
auxiliary,
electric,
hull),
examining
how
different
state-of-the-art
architectures,
ranging
from
basic
machine
learning
models
advanced
deep
techniques
aim
address
them.
Additionally,
concepts
centralized
learning,
federated,
transfer
are
also
discussed,
demonstrating
their
potential
enhance
systems
well
limitations.
Finally,
current
challenges
hindering
adoption
together
with
future
directions
advance
implementation
field.
Sensors,
Journal Year:
2023,
Volume and Issue:
23(3), P. 1409 - 1409
Published: Jan. 27, 2023
Recently,
there
has
been
a
growing
interest
in
issues
related
to
maintenance
performance
management,
which
is
confirmed
by
significant
number
of
publications
and
reports
devoted
these
problems.
However,
theoretical
application
studies
indicate
lack
research
on
the
systematic
literature
reviews
surveys
that
would
focus
evolution
Industry
4.0
technologies
used
area
cross-sectional
manner.
Therefore,
paper
existing
present
an
up-to-date
content-relevant
analysis
this
field.
The
proposed
methodology
includes
bibliometric
review
literature.
First,
general
was
conducted
based
Scopus
Web
Science
databases.
Later,
search
performed
using
Primo
multi-search
tool
following
Preferred
Reporting
Items
for
Systematic
Reviews
Meta-Analyses
(PRISMA)
guidelines.
main
inclusion
criteria
included
publication
dates
(studies
published
from
2012–2022),
English,
found
selected
In
addition,
authors
focused
work
within
scope
Maintenance
study.
papers
fields
were
selected:
(a)
augmented
reality,
(b)
virtual
(c)
system
architecture,
(d)
data-driven
decision,
(e)
Operator
4.0,
(f)
cybersecurity.
This
resulted
selection
214
most
relevant
investigated
area.
Finally,
articles
categorized
into
five
groups:
(1)
Data-driven
decision-making
(2)
(3)
Virtual
Augmented
reality
maintenance,
(4)
(5)
Cybersecurity
maintenance.
obtained
results
have
led
specify
problems
trends
analyzed
identify
gaps
future
investigation
academic
engineering
perspectives.