International Journal of Green Energy,
Journal Year:
2023,
Volume and Issue:
21(4), P. 771 - 786
Published: May 29, 2023
Wind
turbines
are
becoming
increasingly
important
in
the
generation
of
clean,
renewable
energy
worldwide.
To
ensure
their
dependable
and
accessible
operation,
advanced
real-time
condition
monitoring
technology
must
be
implemented
to
guarantee
efficient
wind
power
financial
viability.
Machine
learning
(ML)
has
emerged
as
a
crucial
technique
for
systems
recent
years.
This
is
especially
relevant
because
dedicated
systems,
primarily
focused
on
vibration
measurements,
prohibitively
expensive.
Preventive
maintenance
most
effective
way
detect
address
issues
before
they
impact
performance.
article
provides
comprehensive
up-to-date
review
latest
technologies
fault
detection,
diagnosis,
prognosis
turbines,
with
particular
focus
ML
algorithms
critical
faults
failure
modes,
preprocessing
methods,
evaluation
metrics.
Numerous
references
have
been
analyzed
evaluate
past,
present,
potential
future
research
development
trends
this
field.
Most
these
based
journal
articles,
theses,
reports
found
open
literature.
Big Data,
Journal Year:
2020,
Volume and Issue:
9(1), P. 3 - 21
Published: Dec. 4, 2020
Time
series
forecasting
has
become
a
very
intensive
field
of
research,
which
is
even
increasing
in
recent
years.
Deep
neural
networks
have
proved
to
be
powerful
and
are
achieving
high
accuracy
many
application
fields.
For
these
reasons,
they
one
the
most
widely
used
methods
machine
learning
solve
problems
dealing
with
big
data
nowadays.
In
this
work,
time
problem
initially
formulated
along
its
mathematical
fundamentals.
Then,
common
deep
architectures
that
currently
being
successfully
applied
predict
described,
highlighting
their
advantages
limitations.
Particular
attention
given
feed
forward
networks,
recurrent
(including
Elman,
long-short
term
memory,
gated
units,
bidirectional
networks),
convolutional
networks.
Practical
aspects,
such
as
setting
values
for
hyper-parameters
choice
suitable
frameworks,
successful
also
provided
discussed.
Several
fruitful
research
fields
analyzed
obtained
good
performance
reviewed.
As
result,
gaps
been
identified
literature
several
domains
application,
thus
expecting
inspire
new
better
forms
knowledge.
Earthquake Spectra,
Journal Year:
2020,
Volume and Issue:
36(4), P. 1769 - 1801
Published: June 3, 2020
Machine
learning
(ML)
has
evolved
rapidly
over
recent
years
with
the
promise
to
substantially
alter
and
enhance
role
of
data
science
in
a
variety
disciplines.
Compared
traditional
approaches,
ML
offers
advantages
handle
complex
problems,
provide
computational
efficiency,
propagate
treat
uncertainties,
facilitate
decision
making.
Also,
maturing
led
significant
advances
not
only
main-stream
artificial
intelligence
(AI)
research
but
also
other
engineering
fields,
such
as
material
science,
bioengineering,
construction
management,
transportation
engineering.
This
study
conducts
comprehensive
review
progress
challenges
implementing
earthquake
domain.
A
hierarchical
attribute
matrix
is
adopted
categorize
existing
literature
based
on
four
traits
identified
field,
method,
topic
area,
resource,
scale
analysis.
The
state-of-the-art
indicates
what
extent
been
applied
areas
engineering,
including
seismic
hazard
analysis,
system
identification
damage
detection,
fragility
assessment,
structural
control
for
mitigation.
Moreover,
associated
future
needs
are
discussed,
which
include
embracing
next
generation
sharing
sensor
technologies,
more
advanced
techniques,
developing
physics-guided
models.
Proceedings of the IEEE,
Journal Year:
2022,
Volume and Issue:
110(6), P. 754 - 806
Published: May 13, 2022
Wind
turbines
play
an
increasingly
important
role
in
renewable
power
generation.
To
ensure
the
efficient
production
and
financial
viability
of
wind
power,
it
is
crucial
to
maintain
turbines'
reliability
availability
(uptime)
through
advanced
real-time
condition
monitoring
technologies.
Given
their
plurality
evolution,
this
article
provides
updated
comprehensive
review
state-of-the-art
technologies
used
for
fault
diagnosis
lifetime
prognosis
turbines.
Specifically,
presents
major
failure
modes
observed
along
with
root
causes,
thoroughly
reviews
techniques
strategies
available
turbine
from
signal-based
model-based
perspectives.
In
total,
more
than
390
references,
mostly
selected
recent
journal
articles,
theses,
reports
open
literature,
are
compiled
assess
as
exhaustively
possible
past,
current,
future
research
development
trends
substantial
active
investigation
area.
Chinese Journal of Aeronautics,
Journal Year:
2023,
Volume and Issue:
37(7), P. 24 - 58
Published: Dec. 12, 2023
Multi-Source
Information
Fusion
(MSIF),
as
a
comprehensive
interdisciplinary
field
based
on
modern
information
technology,
has
gained
significant
research
value
and
extensive
application
prospects
in
various
domains,
attracting
high
attention
interest
from
scholars,
engineering
experts,
practitioners
worldwide.
Despite
achieving
fruitful
results
both
theoretical
applied
aspects
over
the
past
five
decades,
there
remains
lack
of
systematic
review
articles
that
provide
an
overview
recent
development
MSIF.
In
light
this,
this
paper
aims
to
assist
researchers
individuals
interested
gaining
quick
understanding
relevant
techniques
trends
MSIF,
which
conducts
statistical
analysis
academic
reports
related
achievements
MSIF
two
provides
brief
theories,
methodologies,
well
key
issues
challenges
currently
faced.
Finally,
outlook
future
directions
are
presented.
