Structural Health Monitoring,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 30, 2025
Rolling
bearings
are
critical
components
in
rotating
machinery.
Throughout
their
operational
life,
they
endure
periodic
loading
cycles
that
could
lead
to
the
formation
of
spalls.
While
current
capabilities
enable
early
detection
incipient
spalls,
which
helps
prevent
catastrophic
failure
machine,
utilize
entire
life
bearings,
it
is
essential
estimate
spall
severity
and
remaining
useful
life.
Using
physics-based
models
experimental
results,
this
article
introduces
an
integrative
approach.
We
develop
a
new
conceptual
framework
for
monitoring
bearing
health,
assessing
defect
by
identifying
physical
processes
govern
evolution,
predicting
real-world
applications.
The
incorporates
four
models:
dynamic
model,
oil
debris
(ODM)
damage
finite
element
along
with
work,
including
vibration
analysis,
ODM
data,
strain
measurements
using
fiber
Bragg
grating
sensors.
integration
work
these
provides
condition
health
indicators
both
diagnosis
prognosis.
By
research
community
can
gain
deeper
understanding
propagation
mechanisms,
will
result
better
predictions
regarding
rolling
bearings.
Structural Health Monitoring,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 13, 2025
Characterizing
equipment
performance
degradation
and
predicting
remaining
useful
life
(RUL)
are
critical
aspects
of
predictive
maintenance
in
mechanical
systems.
The
foundation
effective
RUL
prediction
lies
constructing
health
indicator
(HI)
based
on
condition
monitoring
signals
that
accurately
reflect
status.
In
addition,
the
individual
variability
uncertainty
process
often
make
it
challenging
for
a
single
path
to
represent
entire
fully.
To
address
these
issues,
this
article
introduces
novel
framework
characterization
prediction.
Initially,
we
constructed
HI
using
Wasserstein
distance
Cumulative
sum
(CUMSUM)
control
chart.
This
approach
not
only
captures
changes
signal
probability
distribution
during
but
also
exhibits
strong
monotonicity,
trendability,
robustness.
Next,
propose
dynamic
first
time
(FPT)
identification
method
Chebyshev’s
inequality,
which
effectively
mitigates
influence
outliers
minor
fluctuations.
Additionally,
develop
matching
multipath
adaptive
drift
linear
multifractional
Lévy
stable
motion
(DPM-MPALMLSM)
model
MPALMLSM
incorporates
multiple
paths
capture
non-Gaussian
characteristics,
long-range
dependence
features,
multifractal
properties
process,
with
coefficients
dynamically
updated
as
data
evolves.
method,
grounded
evaluation,
facilitates
efficient
switching
between
paths,
enhancing
accuracy.
effectiveness
precision
proposed
demonstrated
full-life
testing
from
heavy
truck
transmissions,
XJTU-SY
IMS
benchmark
bearing
datasets.
Structural Health Monitoring,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 30, 2025
Rolling
bearings
are
critical
components
in
rotating
machinery.
Throughout
their
operational
life,
they
endure
periodic
loading
cycles
that
could
lead
to
the
formation
of
spalls.
While
current
capabilities
enable
early
detection
incipient
spalls,
which
helps
prevent
catastrophic
failure
machine,
utilize
entire
life
bearings,
it
is
essential
estimate
spall
severity
and
remaining
useful
life.
Using
physics-based
models
experimental
results,
this
article
introduces
an
integrative
approach.
We
develop
a
new
conceptual
framework
for
monitoring
bearing
health,
assessing
defect
by
identifying
physical
processes
govern
evolution,
predicting
real-world
applications.
The
incorporates
four
models:
dynamic
model,
oil
debris
(ODM)
damage
finite
element
along
with
work,
including
vibration
analysis,
ODM
data,
strain
measurements
using
fiber
Bragg
grating
sensors.
integration
work
these
provides
condition
health
indicators
both
diagnosis
prognosis.
By
research
community
can
gain
deeper
understanding
propagation
mechanisms,
will
result
better
predictions
regarding
rolling
bearings.
