Efficient Plant April 2022 - 22

department | tech horizons
An ongoing look into the
technologies and trends
that will shape your
manufacturing future
Machine W
Learning to
Play Key
RCM Role
Dr. Klaus M. Blache
Univ. of Tennessee Reliability and
Maintainability Center (RMC)
Rajiv Anand
CEO, Quartic.ai
ITH SO MUCH of what
we need to fix attributed
to random failure, more
sophisticated tools and technologies
are needed to enable the best opportunity
for early detection and intervention.
Understanding this, the quest for
predictive maintenance began decades
ago but overall progress has been
limited. Rajiv Anand, CEO of Quartic.
ai, comments on the current state and
benefits of machine learning.
The first leg of the journey was to
move away from time-based maintenance
to condition-based monitoring
(CBM). This journey started with routebased
condition data collection and then
moved to using wired online sensors.
Today, the availability of inexpensive
wireless sensors is rapidly increasing
access to more condition data.
The next step is implementing reliMachine
learning and artificial intelligence will play
a significant role in taking your reliability-centered
maintenance program to the next level.
ability-centered maintenance (RCM), an
approach that Nowlan and Heap started
for the aircraft industry, and was later
enriched, and " industrialized " by John
Moubray. It's in this step that machine
learning will play a significant role.
Let's first look at it from an RCM
perspective. Two key tenets of RCM are
risk and FMECA (failure mode effects
and criticality analysis). The fundamental
value proposition of RCM is that
successful implementation will lead to
a greater understanding of the level of
risk that the organization is managing
(https://en.wikipedia.org/wiki/Reliabilitycentered_maintenance#cite_noteRCM2-2).
Risk
is defined as severity x occurrence
x detectability. The purpose of
collecting route-based or sensor-based
condition data is to increase detectability.
We want to be able to detect as many
critical failure mechanisms as possible,
as early as possible, and under all operating
contexts of the equipment.
Analysis and interpretation of the
condition data is what provides the
detectability. This analysis is still largely
performed by machinery health analysts.
We owe a great debt of gratitude for the
hard work they perform to keep our
businesses profitable, safe, and sustainable.
Unfortunately, there weren't
enough of them to begin with and not
enough are entering the workforce.
Any machinery health analyst will
speak to the nightmarish challenges of
analyzing condition data with constantly
changing baselines. Their life is becoming
more complex and stressful as the
amount of condition data increases and,
with the demand for agility in supply
chains, changing operating contexts
become the norm.
22 | EFFICIENTPLANTMAG.COM
APRIL 2022
https://en.wikipedia.org/wiki/Reliabilitycentered_maintenance#cite_noteRCM2-2 http://www.Quartic.ai http://www.EFFICIENTPLANTMAG.COM

Efficient Plant April 2022

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