Tech Briefs Magazine - August 2022 - 24

Operational Intelligence
biases, and traditional computing can
only do what it is told.
Math, Physics,
& Science
Modern AI systems are a form of
computing that dynamically updates
its rules based on machine learning
(ML) techniques, building on massive
datasets. Therefore, AI is much more
capable than standard logic-solving
systems, and it can also evolve. But in
many cases, especially those involving
the control of real-world industrial
equipment, a pure AI solution would
not be ideal.
AIoT
IoT & Cloud
SaaS
Machine
Learning
The answer is to implement AI solutions
that provide tools and analytics to
help operational experts shape AI rules
and deploy the resulting information.
AIoT Architecture
Figure 1: Operational intelligence combines the best of human intelligence and AI, enabled by artificial
intelligence of things (AIoT) functionality created at the intersection of traditional and new
sciences and technologies. (Image: Koidra)
To nurture this type of improved " operational
intelligence, " it is now practical
to apply modern artificial intelligence
(AI) techniques, in conjunction
with the industrial internet of things
(IIoT) technologies, to upgrade the capabilities
of traditional industrial automation.
With a digital decision-making
framework built from innovative hardware
and software architectures, automation
platforms can take advantage
of user intelligence, traditional hard
sciences, and modern technologies to
create artificial intelligence of things
(AIoT) systems (Figure 1).
AI and Humans Together
Humans are good at making decisions
based on limited data, while computing
systems can analyze massive
datasets and meticulously execute
rules. However, humans are subject to
An industrial AIoT system must include
local micro-control (μcontrol) and
higher-level supervisory macro-control.
Installed in the field near the sensors
and actuators, μcontrollers can be traditional
programmable logic controllers
(PLCs), or newer options based on Arduinos,
Raspberry Pi, or other hardware.
The μcontrollers are necessary to
provide near-real-time data gathering
and control.
Because μcontrollers lack the processing
power needed for AI, it is necessary
to have an on-premises or cloudbased
computing solution running the
macro-control functions. These functions
include analytics, advanced visualization
for the operators, and the
development of AI algorithms. The
resulting setpoints and AI rules are
transmitted to the lower level μcontrollers
to execute the functionality in
the field.
Traditional automation methods are
capable, using PLCs in conjunction with
Figure 2: Modern AIoT for industrial automation features tools such as this Excel-like interface, which empowers operators to analyze operational
data, create insights, and update plant functionality. (Image: Koidra)
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