Efficient Plant October 2021 - 9

feature | digital transformation
The terms AI and ML (machine learning)
are often used interchangeably, which can
be confusing. AI is the overarching science
of making machines and physical systems
smarter by embedding artificial intelligence
in them. ML is a subset of AI that involves
systems gaining knowledge over time
through self-learning to become smarter
and more predictable, without human
intervention.
As an example, consider a motor, which is
To acquire a holistic overview of an asset,
integrate information from OT/IT/ET systems
and store the relevant pieces in a contextualized
data model. This makes it possible to visualize
and activate optimum equipment operation for
the best overall process results.
lake. Next, contextualize and store related
data in an industry-specific data model,
such as paper making or plastic extruding.
Then apply advanced analytics and industrial
AI algorithms to identify correlations that
were not previously visible.
Industrial artificial intelligence (AI)
can play a major role in identifying these
patterns and making process predictions.
OCTOBER 2021
an essential and omnipresent manufacturing
asset. In terms of data, we have:
OT data: Motor speed, vibration level,
and bearing temperature are typical parameters
monitored in real time by OT systems
to report how the motor is performing. This
normally comes from automation system
components such as PLCs or a DCS.
IT data: If we want to see things such as
the motor's maintenance history, when it was
last serviced, how much has been spent on
repairs, or if the right bearings are in stock,
we must find it in various IT systems, usually
somewhere in the ERP solution.
ET data: Information about factors such as
whether the motor is within its design speed
limits, how much vibration it can take, the
safe operating temperature for its bearings,
or what the useful bearing life should be, is
all residing within the ET (or engineering
design) systems.
To acquire a holistic overview of the
motor, integrate information from all these
systems and store the relevant pieces in a
contextualized data model. This allows visualization
and the ability to activate optimum
equipment operation for the best overall
process results.
INTEGRATION IS CRITICAL
A motor is just one example. In a large
manufacturing plant, there can be hundreds
of such assets performing several functions
and running under different operating
conditions with varied design parameters,
all with data stored in various systems.
Widespread OT/IT/ET integration and contextualization
is critical to obtain a complete
view of the plant and carry out valuable
analytical tasks that improve operations,
asset integrity and performance management,
safety, sustainability, and supplychain
functions. What emerges are patterns
that accurately predict future behavior,
allowing improved process performance.
We've been using AI/ML to deliver a
higher degree of prediction accuracy and
optimization to operations, processes, and
assets. Combining AI with deep industrial
domain expertise empowers operators to
run their industrial processes safer, more
effectively, and more sustainably.
There are several barriers-perceived and
otherwise-that hinder implementation of
advanced analytics. The most common reason
for hesitation is the perceived complexity.
People mistakenly think it is much more
difficult to achieve than it is. Another is the
incorrect belief that, to use big data, you
must make massive capital expenditures,
because it's an " all or nothing " undertaking.
It isn't. You can start with small steps.
Other reasons might be lack of cooperation
between OT, IT, and ET people, and just
generally slow adoption of new digital tools
in many industrial sectors.
The fact is that it's easy to join this digital-mining
journey, no matter where you
are, using data and signals that are already
available in your process-control, business,
and engineering systems. EP
Rajesh Ramachandran, CDO at ABB Process
Automation, Zurich (U.S. headquarters in
Houston, abb.com), focuses on implementing
new strategies to obtain greater value from
vast data silos in manufacturing companies.
He drives the creation of new digital
platforms that simplify the task of extracting
targeted data to successfully predict outcomes
and optimize manufacturing processes.
EFFICIENTPLANTMAG.COM | 9
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Efficient Plant October 2021

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