IEEE Transactions on Instrumentation and Measurement,
Journal Year:
2024,
Volume and Issue:
73, P. 1 - 10
Published: Jan. 1, 2024
In
next-generation
aircraft,
Electro-Mechanical
Actuators
(EMAs)
are
increasingly
used.
But
the
safety
of
EMA
is
not
sufficient
for
primary
flight
control
actuation
aircraft.
One
effective
way
to
improve
develop
Prognostics
and
Health
Management
(PHM).
However,
variable
operation
modes
make
it
difficult
implement
high-performance
PHM.
Thus,
need
be
recognized,
but
high
similarity
sensing
data
between
different
making
challenging.
a
new
deep-shallow
fusion
network
with
convolutional
neural
network,
self-attention
mechanism
Bayesian
(CSBN)
proposed
mode
recognition,
which
can
overcome
challenge
multiple
data.
CSBN
based
recognition
method,
statistical
features
firstly
extracted
discretized.
Then,
conducted
discretized
on
CSBN.
Finally,
output
used
as
results.
To
validate
its
effectiveness,
experiments
utilizing
practical
implemented.
Experimental
results
demonstrate
that
suitable
recognition.
IEEE Internet of Things Journal,
Journal Year:
2024,
Volume and Issue:
11(13), P. 23002 - 23019
Published: March 18, 2024
With
the
widespread
application
of
deep
learning
in
Internet
Things
(IoT),
remarkable
achievements
have
been
made
especially
rolling
bearing
fault
diagnosis
rotating
machinery.
However,
such
complex
models
commonly
high
demand
for
a
large
number
parameters
and
computational
resources,
with
insufficient
interpretability,
which
restrict
their
extensive
real-world
industrial
applications.
To
improve
efficiency
this
study
innovatively
fuses
quadratic
neural
network
(QNN)
bidirectional
long
short-term
memory
(Bi-LSTM)
to
develop
novel
hybrid
model
quick
accurate
faults.
The
results
show
that
fully
utilizes
multilayer
feature
extraction
QNN
sensitivity
Bi-LSTM
dynamic
evolution
signals
significantly
accuracy
speed
diagnosis.
By
visualizing
convolutional
kernel
response
map,
Qttention
mapping
QNN,
hidden
states
Bi-LSTM,
makes
progress
interpretability
successfully
demonstrates
model's
attention
different
features
signals,
provides
users
more
reasonable
understanding
interpretation
results.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 57574 - 57602
Published: Jan. 1, 2024
Predictive
maintenance
is
a
well
studied
collection
of
techniques
that
aims
to
prolong
the
life
mechanical
system
by
using
artificial
intelligence
and
machine
learning
predict
optimal
time
perform
maintenance.
The
methods
allow
maintainers
systems
hardware
reduce
financial
costs
upkeep.
As
these
are
adopted
for
more
serious
potentially
life-threatening
applications,
human
operators
need
trust
predictive
system.
This
attracts
field
Explainable
AI
(XAI)
introduce
explainability
interpretability
into
XAI
brings
can
amplify
in
users
while
maintaining
well-performing
systems.
survey
on
explainable
(XPM)
discusses
presents
current
as
applied
following
Preferred
Reporting
Items
Systematic
Reviews
Meta-Analyses
(PRISMA)
2020
guidelines.
We
categorize
different
XPM
groups
follow
literature.
Additionally,
we
include
challenges
discussion
future
research
directions
XPM.
IEEE Internet of Things Journal,
Journal Year:
2024,
Volume and Issue:
11(13), P. 22915 - 22925
Published: Feb. 5, 2024
The
development
of
Internet
Things
technology
provides
abundant
data
resources
for
prognostics
health
management
industrial
machinery,
and
data-driven
methods
have
shown
their
powerful
ability
in
the
field
fault
diagnosis.
However,
these
several
limitations:
1)
Using
less
labeled
to
obtain
higher
accuracy
is
a
challenging
task,
which
limits
application
diagnostic
models
practical
applications.
2)
Physics-informed
knowledge
largely
ignored
during
modeling
process,
contains
wealth
information
that
can
reflect
harmonic
drive's
status.
To
address
challenges,
self-supervised
diagnosis
framework
developed
by
integrating
prior
with
deep
learning
improve
reliability
Specifically,
physics-based
including
32-dimensional
time
domain,
frequency
time-frequency
domain
features,
first
designed
provide
significantly
reduce
amount
required
learning.
Furthermore,
embedded
auto-encoder
network
built
employing
multi-scale
convolutional
auto-encoder.
With
integrate
mechanism,
proposed
method
strong
tool
representation
an
effective
solution
under
few-shot
scenario.
experimental
results
conducted
on
real
drive
dataset
prove
insights
has
excellent
generalizability
Measurement Science and Technology,
Journal Year:
2024,
Volume and Issue:
35(6), P. 062002 - 062002
Published: March 19, 2024
Abstract
Mechanical
fault
diagnosis
is
crucial
for
ensuring
the
normal
operation
of
mechanical
equipment.
With
rapid
development
deep
learning
technology,
methods
based
on
big
data-driven
provide
a
new
perspective
machinery.
However,
equipment
operates
in
condition
most
time,
resulting
collected
data
being
imbalanced,
which
affects
performance
diagnosis.
As
approach
generating
data,
generative
adversarial
network
(GAN)
can
effectively
address
issues
limited
and
imbalanced
practical
engineering
applications.
This
paper
provides
comprehensive
review
GAN
Firstly,
GAN-based
diagnosis,
basic
theory
various
variants
(GANs)
are
briefly
introduced.
Subsequently,
GANs
summarized
categorized
from
labels
models,
corresponding
applications
outlined.
Lastly,
limitations
current
research,
future
challenges,
trends
selecting
application
discussed.
