Transactions of the Canadian Society for Mechanical Engineering,
Journal Year:
2024,
Volume and Issue:
48(1), P. 132 - 145
Published: Jan. 24, 2024
The
sliding
contact
model
and
dynamic
between
ball
outer
ring
are
established
to
investigate
the
ceramic
bearing
vibration
characteristic
when
slides.
periodic
rules
of
system
analyzed
by
calculation
results
model.
heat
generation
slippage
is
established,
influence
mechanism
temperature
oil
film
viscosity
caused
analyzed.
It
can
be
seen
from
that
deformation
increases
with
increase
radial
load,
smaller
than
Hertz
An
experimental
platform
for
testing
under
load
conditions
established.
show
variation
rule
large
consistent
average
error
amplitude
only
1.09%
1.3%
at
10000
r/min.
in
this
paper
simulate
slides,
provides
a
certain
theoretical
basis
research
rotating
machinery
condition.
IEEE Transactions on Instrumentation and Measurement,
Journal Year:
2023,
Volume and Issue:
72, P. 1 - 11
Published: Jan. 1, 2023
The
digital
twin
of
life-cycle
rolling
bearing
is
significant
for
its
degradation
performance
analysis
and
condition
prediction.
To
solve
the
problem
which
not
reliable
to
arrange
production
cycle
by
predicting
diagnostic
results
in
existing
studies,
because
it
accurate
only
consider
single-scale
fault
modeling.
It
studied
that
multiscale
evolution
law
close
true
involves
microscopic
cracks,
mesoscopic
spall,
macroscopic
defect,
establishing
model
with
outer
ring
fault.
Based
on
measured
signals
dynamic
fault,
time-varying
2-D
sizes
faults
are
estimated.
mapping
relationship
between
dimensions
established
using
BP
network,
progressive
mechanism
whole
life
analyzed.
Then,
substituting
excitation
evolutionary
into
model,
virtual
space.
real-time
update
realized
integrating
sensor
data
faulty
bearings
subspace.
accuracy
verified
comparing
twinning
time
domain
signals.
proposed
improve
efficiency
extension
accurately.
International Journal of Computer Integrated Manufacturing,
Journal Year:
2024,
Volume and Issue:
38(1), P. 79 - 115
Published: Feb. 22, 2024
Quality
control
methods
and
techniques
have
been
investigated
in
different
fields
of
manufacturing
during
the
last
decades.
The
introduction
robots
processes
has
created
a
rapid
deployment
robotic
applications,
leading
to
an
increased
research
interest
aspect
quality
such
industrial
environments.
This
paper
summarizes
presents
review
recent
progress
on
technologies,
focusing
robot-based
production
systems.
role
parameters
affecting
tasks
is
also
discussed,
incorporating
impact
operator
support
systems
human
robot
collaborative
Research
gaps
implications
applications
are
described,
future
outlook
particular
field
provided.
Measurement Science and Technology,
Journal Year:
2024,
Volume and Issue:
36(1), P. 012003 - 012003
Published: Oct. 21, 2024
Abstract
Real-time
control
systems
(RTCSs)
have
become
an
indispensable
part
of
modern
industry,
finding
widespread
applications
in
fields
such
as
robotics,
intelligent
manufacturing
and
transportation.
However,
these
face
significant
challenges,
including
complex
nonlinear
dynamics,
uncertainties
various
constraints.
These
challenges
result
weakened
disturbance
rejection
reduced
adaptability,
which
make
it
difficult
to
meet
increasingly
stringent
performance
requirements.
In
fact,
RTCSs
generate
a
large
amount
data,
presents
important
opportunity
enhance
effectiveness.
Machine
learning,
with
its
efficiency
extracting
valuable
information
from
big
holds
potential
for
RTCSs.
Exploring
the
machine
learning
is
great
importance
guiding
scientific
research
industrial
production.
This
paper
first
analyzes
currently
faced
by
RTCSs,
elucidating
motivation
integrating
into
systems.
Subsequently,
discusses
aspects,
system
identification,
controller
design
optimization,
fault
diagnosis
tolerance,
perception.
The
indicates
that
data-driven
methods
exhibit
advantages
addressing
multivariable
coupling
characteristics
systems,
well
arising
environmental
disturbances
faults,
thereby
effectively
enhancing
system’s
flexibility
robustness.
compared
traditional
methods,
also
faces
issues
poor
model
interpretability,
high
computational
requirements
leading
insufficient
real-time
performance,
strong
dependency
on
high-quality
data.
proposes
future
directions.
Measurement Science and Technology,
Journal Year:
2024,
Volume and Issue:
35(9), P. 095103 - 095103
Published: May 28, 2024
Abstract
Deep
learning-based
fault
diagnosis
methods
for
rolling
bearings
are
widely
utilized
due
to
their
high
accuracy.
However,
they
have
limitations
under
conditions
with
few
samples.
To
address
this
problem,
a
model-data
combination
driven
digital
twin
model
(MDCDT)
is
proposed
in
work
samples
of
bearings.
The
simulation
signals
generated
by
different
dynamic
models
and
the
measured
mixed
through
MDCDT.
MDCDT
generates
virtual
bridge
gap
between
simulated
combining
respective
advantages.
This
paper
also
proposes
image
coding
method
based
on
Markov
transfer
matrix
(MTMIC)
convert
one-dimensional
vibration
into
two-dimensional
images
both
frequency
domain
information
time
information,
making
it
easier
extract
features
neural
network
training.
In
end,
developed
was
evaluated
using
real
bearing
data.
Experiments
show
that
can
generate
data
diagnosis,
accuracy
significantly
improved.
