Efficient Plant April 2022 - 23

department | tech horizons
We need to add more advanced tools
to their toolkit and eliminate some of the
mundane analytical tasks from their task
list. Th at's where machine learning (ML) and
artifi cial intelligence (AI) come in.
ML AND AI
In industry 3.0 our focus was on automating
manual tasks. Among other things,
the goals of Industry 4.0 are achieved by
automating cognitive tasks. Machine learning,
a key element of Industry 4.0, can be
loosely defi ned as " automation of cognitive
tasks. " As mentioned previously, increasing
detectability with machinery health
analysis is largely a cognitive task. Using
machine learning to increase detectability
is, therefore, a logical and natural step in
the journey to predictive maintenance.
When we increase detectability with
condition-based monitoring, whether it's
route- or sensor-based, it is always with the
point " P " on the I-P-F curve. Th is is because
condition-based measurements only allow us
to go that far up the curve. Can we go farther
up? Of course we can and, if we are to achieve
the true objectives of RCM, we must.
Th e basic defi nition of reliability is a
measure of the ability of an asset to perform
its intended function. Any time that ability is
degraded, the asset becomes unreliable. We
can and must start measuring function degradation
as early as possible. Machine learning
increases detectability. Condition sensors
(traditional CBM) can deliver operational
context to CBM and establish baselines with
an infi nite set of rules and increased accuracy
using multiple techniques (vibration +
tribology, for example). ML can move the " P "
farther up the curve using operational data.
Th e fi xation with " predicting " failures and
the bold claims today of predicting failure x
days/weeks/months in advance with machine
APRIL 2022
ML detectability opportunity
Early detectability
Failure onset
p1 p2
p3
Machine
learning +
fusion AI early
alerts
p4
Condition data alerts
p5
p6
Easier detectability
Time
Everything
is fi ne. ML is
watching for
functional
degradation
Functional
degradation
Traditional CBM
Functional failure
Source: Adapted from copyright 2018-22 Quartic.
Machine learning will improve detectability, making it easier to move further up the I-P-F
curve toward initial failure onset.
learning are just frivolous-they do not
accomplish anything for RCM or risk management.
Measuring degradation of reliable
function, the risk that is being accumulated,
and the risk trajectory allow us to achieve
RCM objectives, resulting in effi cient, safe,
reliable, and sustainable operations.
Machine learning can be used by
machinery-health analysts, reliability,
and RCM practitioners, with and without
condition sensors, to increase detectability.
Machine learning is not magical. Just like any
other tool, it must be used properly by trained
practitioners. Instead of performing machinery-health
analytics, the practitioners can
train an army of health-monitoring agents.
Th e practitioner determines, based on their
knowledge of RCM principles, the equipment,
and the process, which agents should be built,
how they should be deployed, when they
should be retrained, what detectability they
should increase, how frequently they should
alert whom and, most important, what action
they should inform.
Combining machine learning with rules,
complex event processing, and risk calculation
based on severity in a coherent platform
can lead to RCM success. At the risk of
coining another term, let's call this integrated
system a digital RCM system. EP
Interested in learning more? Join us July 13
and 14 for a two-day course that will prepare
you to implement ML and AI for digital asset
performance monitoring in your Industry 4.0
initiatives. Visit rmc.utk.edu/events/.
Based in Knoxville, Dr. Klaus M. Blache is
director of the Reliability & Maintainability
Center at the Univ. of Tennessee, and
a research professor in the College of
Engineering. Contact him at
kblache@utk.edu.
Rajiv Anand is the co-founder and CEO
of Quartic.ai, San Jose, CA (quartic.ai),
provider of a smart-industry platform
for process-manufacturing industries
that implements digital transformation
with AI and IIoT.
EFFICIENTPLANTMAG.COM | 23
Asset health
Condition sensors
http://rmc.utk.edu/events/ http://www.Quartic.ai http://www.quartic.ai http://www.EFFICIENTPLANTMAG.COM

Efficient Plant April 2022

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