IEEE Transactions on Transportation Electrification,
Год журнала:
2023,
Номер
10(2), С. 4421 - 4431
Опубликована: Сен. 11, 2023
Convolutional
neural
networks
(CNN)
have
developed
rapidly
in
recent
years,
which
has
greatly
promoted
the
advancement
of
intelligent
fault
diagnosis.
Most
currently
available
CNN-based
diagnostic
models
are
under
presumption
that
acquired
mechanical
signals
invulnerable
to
noise.
However,
transmission
systems
usually
operate
fluctuating
conditions
(e.g.,
variable
speed
and
strong
noise
scenarios),
making
fault-related
pulse
information
signal
easily
swamped
by
Therefore,
it
is
challenging
for
these
existing
approaches
achieve
satisfactory
results
industrial
scenarios.
To
deal
with
this
problem,
an
online
knowledge
distillation-based
multiscale
threshold
denoising
network
(OKD-MTDN)
research
work.
The
main
innovations
contributions
work
include:
1)
introducing
a
novel
convolutional
module,
called
Multiscale
Module
(MCM),
alongside
Global
Attention
(GAM),
extracting
range
discriminative
features
generated
from
signals;
2)
designing
multi-dilated
module
(MTDM)
expand
receptive
field
filter
out
interference
features;
3)
establishing
distillation
(OKD)
algorithm
improve
generalization
capability
OKD-MTDN.
hF-MS
planetary
gearbox
dataset
real-running
high-speed
rail
utilized
verify
effectiveness
proposed
method.
Experimental
show
OKD-MTDN
can
various
nonstationary
IEEE Transactions on Industrial Cyber-Physical Systems,
Год журнала:
2023,
Номер
1, С. 113 - 122
Опубликована: Янв. 1, 2023
The
fault
diagnosis
of
rolling
bearings
is
utmost
importance
in
industrial
applications
to
ensure
mechanical
systems'
reliability,
safety,
and
economic
viability.
However,
conventional
data-driven
techniques
mainly
depend
on
a
pre-existing
dataset
with
complete
failure
modes
knowledge
serve
as
the
training
data,
which
may
not
be
available
or
accessible
some
crucial
scenarios.
This
can
limit
practicality
these
methodologies
real-world
applications.
article
addresses
this
issue
by
developing
novel
digital
twin-enabled
domain
adversarial
graph
network
(DT-DAGN).
main
contributions
are
follows:
1)
development
comprehensive
accurate
twin
model
for
that
includes
dynamic
simulation
bearing's
operational
status
using
only
its
structural
parameters
severity/size
obtain
system's
vibration
response,
2)
convolutional
network-based
transfer
learning
framework
from
simulated
datasets
measured
datasets,
enabling
effective
diagnostics
limited
knowledge.
A
series
experiments
applied
validate
efficacy
developed
methodology.
Information Fusion,
Год журнала:
2023,
Номер
103, С. 102136 - 102136
Опубликована: Ноя. 10, 2023
Advancements
in
structural
health
monitoring
(SHM)
techniques
have
spiked
the
past
few
decades
due
to
rapid
evolution
of
novel
sensing
and
data
transfer
technologies.
This
development
has
facilitated
simultaneous
recording
a
wide
range
data,
which
could
contain
abundant
damage-related
features.
Concurrently,
age
omnipresent
started
with
massive
amounts
SHM
collected
from
large-size
heterogeneous
sensor
networks.
The
abundance
information
diverse
sources
needs
be
aggregated
enable
robust
decision-making
strategies.
Data
fusion
is
process
integrating
various
produce
more
useful,
accurate,
reliable
about
system
behavior.
paper
reviews
recent
developments
applied
systems.
theoretical
concepts,
applications,
benefits,
limitations
current
methods
challenges
are
presented,
future
trends
discussed.
Furthermore,
set
criteria
proposed
evaluate
contents
original
review
papers
this
field,
road
map
provided
discussing
possible
work.
Measurement Science and Technology,
Год журнала:
2024,
Номер
35(4), С. 042003 - 042003
Опубликована: Янв. 12, 2024
Abstract
Deep
learning
(DL)
has
attained
remarkable
achievements
in
diagnosing
faults
for
rotary
machineries.
Capitalizing
on
the
formidable
capacity
of
DL,
it
potential
to
automate
human
labor
and
augment
efficiency
fault
diagnosis
machinery.
These
advantages
have
engendered
escalating
interest
over
past
decade.
Although
recent
reviews
literature
encapsulated
utilization
DL
rotating
machinery,
they
no
longer
encompass
introduction
novel
methodologies
emerging
directions
as
continually
evolve.
Moreover,
practical
application,
issues
trajectories
perpetually
manifest,
demanding
a
comprehensive
exegesis.
To
rectify
this
lacuna,
article
amalgamates
current
research
trends
avant-garde
while
systematizing
anterior
techniques.
The
evolution
extant
status
machinery
were
delineated,
with
intent
providing
orientation
prospective
research.
Over
bygone
decade,
archetypal
theory
empowered
by
directly
establishing
nexus
between
mechanical
data
conditions.
In
years,
meta
methods
aimed
at
solving
small
sample
scenarios
large
model
transformers
mining
big
features
both
received
widespread
attention
development
field
equipment.
excellent
results
been
achieved
these
two
directions,
there
is
review
summary
yet,
so
necessary
update
Lastly,
predicated
survey
developmental
landscape,
challenges
orientations
are
presented.
