NVIDIA RAPIDS AI Revolutionizes Predictive Maintenance in Manufacturing

The
International
Society
of
Automation
(ISA)
reports
that
5%
of
plant
production
is
lost
annually
due
to
downtime.
This
translates
to
approximately
$647
billion
in
global
losses
for
manufacturers
across
various
industry
segments.
The
critical
challenge
is
predicting
maintenance
needs
to
minimize
downtime,
reduce
operational
costs,
and
optimize
maintenance
schedules,
according
to

NVIDIA
Technical
Blog.

LatentView
Analytics

LatentView
Analytics,
a
key
player
in
the
field,
supports
multiple
Desktop
as
a
Service
(DaaS)
clients.
The
DaaS
industry,
valued
at
$3
billion
and
growing
at
12%
annually,
faces
unique
challenges
in
predictive
maintenance.
LatentView
developed
PULSE,
an
advanced
predictive
maintenance
solution
that
leverages
IoT-enabled
assets
and
cutting-edge
analytics
to
provide
real-time
insights,
significantly
reducing
unplanned
downtime
and
maintenance
costs.

Remaining
Useful
Life
Use
Case

A
leading
computing
device
manufacturer
sought
to
implement
effective
preventive
maintenance
to
address
part
failures
in
millions
of
leased
devices.
LatentView’s
predictive
maintenance
model
aimed
to
forecast
the
remaining
useful
life
(RUL)
of
each
machine,
thus
reducing
customer
churn
and
enhancing
profitability.
The
model
aggregated
data
from
key
thermal,
battery,
fan,
disk,
and
CPU
sensors,
applied
to
a
forecasting
model
to
predict
machine
failure
and
recommend
timely
repairs
or
replacements.

Challenges
Faced

LatentView
faced
several
challenges
in
their
initial
proof-of-concept,
including
computational
bottlenecks
and
extended
processing
times
due
to
the
high
volume
of
data.
Other
issues
included
handling
large
real-time
datasets,
sparse
and
noisy
sensor
data,
complex
multivariate
relationships,
and
high
infrastructure
costs.
These
challenges
necessitated
a
tool
and
library
integration
capable
of
scaling
dynamically
and
optimizing
total
cost
of
ownership
(TCO).

An
Accelerated
Predictive
Maintenance
Solution
with
RAPIDS

To
overcome
these
challenges,
LatentView
integrated
NVIDIA
RAPIDS
into
their
PULSE
platform.
RAPIDS
offers
accelerated
data
pipelines,
operates
on
a
familiar
platform
for
data
scientists,
and
efficiently
handles
sparse
and
noisy
sensor
data.
This
integration
resulted
in
significant
performance
improvements,
enabling
faster
data
loading,
preprocessing,
and
model
training.

Creating
Faster
Data
Pipelines

By
leveraging
GPU
acceleration,
workloads
are
parallelized,
reducing
the
burden
on
CPU
infrastructure
and
resulting
in
cost
savings
and
improved
performance.

Working
in
a
Known
Platform

RAPIDS
utilizes
syntactically
similar
packages
to
popular
Python
libraries
like
pandas
and
scikit-learn,
allowing
data
scientists
to
speed
up
development
without
requiring
new
skills.

Navigating
Dynamic
Operational
Conditions

GPU
acceleration
enables
the
model
to
adapt
seamlessly
to
dynamic
conditions
and
additional
training
data,
ensuring
robustness
and
responsiveness
to
evolving
patterns.

Addressing
Sparse
and
Noisy
Sensor
Data

RAPIDS
significantly
boosts
data
preprocessing
speed,
effectively
handling
missing
values,
noise,
and
irregularities
in
data
collection,
thus
laying
the
foundation
for
accurate
predictive
models.

Faster
Data
Loading
and
Preprocessing,
Model
Training

RAPIDS’s
features
built
on
Apache
Arrow
provide
over
10x
speedup
in
data
manipulation
tasks,
reducing
model
iteration
time
and
allowing
for
multiple
model
evaluations
in
a
short
period.

CPU
and
RAPIDS
Performance
Comparison

LatentView
conducted
a
proof-of-concept
to
benchmark
the
performance
of
their
CPU-only
model
against
RAPIDS
on
GPUs.
The
comparison
highlighted
significant
speedups
in
data
preparation,
feature
engineering,
and
group-by
operations,
achieving
up
to
639x
improvements
in
specific
tasks.

Conclusion

The
successful
integration
of
RAPIDS
into
the
PULSE
platform
has
led
to
compelling
results
in
predictive
maintenance
for
LatentView’s
clients.
The
solution
is
now
in
a
proof-of-concept
stage
and
is
expected
to
be
fully
deployed
by
Q4
2024.
LatentView
plans
to
continue
leveraging
RAPIDS
for
modeling
projects
across
their
manufacturing
portfolio.

Image
source:
Shutterstock