Sensors,
Journal Year:
2024,
Volume and Issue:
24(22), P. 7299 - 7299
Published: Nov. 15, 2024
Fault
detection
and
diagnosis
(FDD)
methods
fault-tolerant
control
(FTC)
have
been
the
focus
of
intensive
research
across
various
fields
to
ensure
safe
operation,
reduce
costs,
optimize
maintenance
tasks.
Unmanned
aerial
vehicles
(UAVs),
particularly
quadcopters
or
quadrotors,
are
often
prone
faults
in
sensors
actuators
due
their
complex
dynamics
exposure
external
uncertainties.
In
this
context,
work
implements
different
FDD
approaches
based
on
Kalman
filter
(KF)
for
fault
estimation
achieve
FTC
quadcopter,
considering
with
nonlinear
behaviors
possibility
simultaneous
occurrences
sensors.
Three
KF
considered
analysis:
linear
KF,
extended
(EKF),
unscented
(UKF),
along
three-stage
adaptive
variations
KF.
methods,
especially
filter,
could
enhance
performance
scenarios
considered.
This
led
a
significant
improvement
safety
reliability
quadcopter
through
architecture,
as
system,
which
previously
became
unstable
presence
faults,
maintain
stable
operation
when
subjected
Sensors,
Journal Year:
2024,
Volume and Issue:
24(22), P. 7303 - 7303
Published: Nov. 15, 2024
Accurate
and
reliable
bearing-fault
diagnosis
is
important
for
ensuring
the
efficiency
safety
of
industrial
machinery.
This
paper
presents
a
novel
method
using
Mel-transformed
scalograms
obtained
from
vibrational
signals
(VS).
The
are
windowed
pass
through
Mel
filter
bank,
converting
them
into
spectrum.
These
subsequently
fed
an
autoencoder
comprising
convolutional
pooling
layers
to
extract
robust
features.
classification
performed
artificial
neural
network
(ANN)
optimized
with
FOX
optimizer,
which
replaces
traditional
backpropagation.
optimizer
enhances
synaptic
weight
adjustments,
leading
superior
accuracy,
minimal
loss,
improved
generalization,
increased
interpretability.
proposed
model
was
validated
on
laboratory
dataset
bearing
testbed
multiple
fault
conditions.
Experimental
results
demonstrate
that
achieves
perfect
precision,
recall,
F1-scores,
AUC
1.00
across
all
categories,
significantly
outperforming
comparison
models.
t-SNE
plots
illustrate
clear
separability
between
different
classes,
confirming
model's
robustness
reliability.
approach
offers
efficient
highly
accurate
solution
real-time
predictive
maintenance
in
applications.
Measurement Science and Technology,
Journal Year:
2024,
Volume and Issue:
35(6), P. 066203 - 066203
Published: March 12, 2024
Abstract
Fault
diagnosis
and
tolerance
are
crucial
for
monitoring
system
health
ensuring
stability
in
industrial
processes.
Challenges
arise
designing
fault
diagnostic
solutions
real-time
processes
with
inherent
nonlinear
dynamic
behaviors,
particularly
when
dealing
multiple
operating
regions
characterized
by
varying
dynamics.
This
article
addresses
this
challenge
proposes
a
tolerant
control
scheme
systems.
The
proposed
approach
integrates
fuzzy-based
realization
technique
subspace-aided
methodology
to
effectively
handle
the
behavior
observed
across
different
operational
scenarios.
A
practical
solution
is
presented,
significantly
reducing
computational
burden
associated
online
diagnostics,
as
parity
vectors
computed
offline
using
available
input–output
data
regions.
During
only
spaces
used
fuzzy
realizations
residual
generation,
leading
significant
reduction
computation.
Numerical
examples
demonstrate
effectiveness
of
method,
achieving
high
precision
rate
diagnostics.
Furthermore,
integrated
fault-tolerant
applications,
demonstrated
application
continuous
stirred
tank
reactor.
integration
enables
tolerate
faults
ensure
sub-optimal
operation
process.
Applied Mathematics and Nonlinear Sciences,
Journal Year:
2025,
Volume and Issue:
10(1)
Published: Jan. 1, 2025
Abstract
Under
the
background
of
increasing
requirements
for
safety,
automation
and
intelligence
in
mining
operations,
real-time
monitoring
fault
diagnosis
underground
electrical
equipment
have
become
particularly
critical.
In
order
to
meet
demand
status
complex
environment,
this
paper
designs
a
set
intelligent
system
architecture
based
on
edge
computing,
which
contains
four
main
sections:
monitoring,
data
processing,
analysis,
control
center.
terms
diagnosis,
studies
GRU
neural
networks
detail,
combines
designed
with
neurons,
constructs
model
paper.
The
is
tested
analyzed.
precision,
recall,
accuracy
paper’s
recognition
are
0.899,
0.913,
0.935,
respectively,
indicating
that
has
excellent
performance
field
recognition.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(9), P. 4829 - 4829
Published: April 27, 2025
With
the
development
of
urbanization,
building
maintenance
units
(BMUs)
have
been
widely
used
in
super
high-rise
buildings.
As
aerial
work
machinery,
condition
monitoring
plays
a
vital
role
safety
and
management
BMUs.
However,
BMUs
multi-source
heterogeneous
data
relationships
that
are
difficult
for
systems
to
understand.
Moreover,
at
this
stage,
there
is
lack
sufficient
samples
support
fault
diagnosis
data.
Therefore,
paper
proposes
real-time
system
BMU
operating
conditions.
This
system,
based
on
Internet
Things
(IoT)
architecture,
acquires
stores
from
distributed
systems,
improving
collection
sharing
rate
throughout
entire
process.
A
collaborative
reasoning
chain
model
was
established
knowledge
sources
process
signals,
which
increased
accuracy
identification
97%.
Finally,
through
simulation
testing
evaluations,
can
stably
transmit
within
6–7
days
accurately
analyze
operational
status
BMU,
with
an
error
5%.
It
effectively
improves
efficiency
also
provides
new
method
practical
application
intelligent
operation
maintenance.
Signals,
Journal Year:
2025,
Volume and Issue:
6(2), P. 18 - 18
Published: April 3, 2025
Fault
diagnosis
is
essential
in
industrial
production.
With
the
advancement
of
IoT
technology,
real-time
data
acquisition
and
storage
have
become
feasible,
enabling
deep
learning-based
fault
methods
to
achieve
remarkable
results.
However,
existing
approaches
often
overlook
temporal
characteristics
occurrences
struggle
with
imbalance
between
normal
faulty
conditions,
impacting
diagnostic
performance.
To
address
these
challenges,
this
paper
proposes
an
integrated
method
that
incorporates
balancing,
feature
extraction,
information
analysis.
The
approach
consists
two
key
components:
(1)
dataset
construction
using
digital
twin
technology
(2)
model
(CNN-BLSTM-attention).
Digital
generates
virtual
under
various
operating
mitigating
small-sample
issue.
proposed
leverages
a
sliding
window
mechanism
capture
both
information,
enhancing
pattern
recognition.
Experimental
results
demonstrate
that,
compared
traditional
methods,
effectively
reduces
noise
interference
achieves
high
accuracy
96.46%,
validating
its
robustness
complex
settings.
This
research
provides
valuable
theoretical
practical
insights
for
improving
equipment
such
as
screw
presses.
