Tech Briefs Magazine - August 2024 - 16

Generative AI
Engineers using Industrial Copilot and the Copilot Interface on screen. (Image: Siemens)
will help identify and solve production
bottlenecks thanks to generative AI's
powerful reasoning and analytical abilities.
Looking at vast quantities of data
across multiple shifts and critical events
an industrial copilot could identify production
bottlenecks and inefficient processes,
while at the same time providing
potential solutions and detailed action
plans for improvement.
It can also interface with predictive
maintenance tools to facilitate asset intelligence
across plants without the
need for manual analysis, helping to
not just minimize downtime but maximize
uptime of monitored equipment.
Predictive maintenance, enabled by AI,
allows companies to both reduce scheduled
maintenance and avoid unplanned
downtime.
A worker is seen interacting with the Industrial Copilot for operations. (Image: Siemens)
Copilots Ensure Smooth
Operations
Keeping a factory operating smoothly
with minimal downtime, where every minute
down incurs extreme cost, is a key concern
and major challenge faced by manufacturing
companies the world over. When
it comes to efficiently handling issues and
proactively
locating problems, data is,
once again, the key. With production machines
and even entire factories growing
smarter and more connected through the
continuing efforts of IT/OT convergence,
data itself is becoming more plentiful
while extracting useful insights from it in a
timely manner remains challenging in
brownfield scenarios. Now, thanks to the
data processing abilities of AI, industrial
copilots can sift through the massive
amounts of available information to extract
key insights, helping minimize downtime
while maximizing uptime.
The technology will allow operators
and maintenance engineers to interact
16
with machines directly, enabling them to
ask the machine about problems or for its
current status like a doctor might ask a
patient. However, unlike a doctor and patient,
a machine augmented by an industrial
copilot can instruct a maintenance
engineer on its own correct course of
treatment, which is to say guidance on
how to get the machine up and running
again. By leveraging AI to index knowledge
from existing documentation, like
worker instructions or manuals, alongside
process and sensor data through
IIoT and edge devices, users can easily be
presented with the most critical and pertinent
information first, leveraging an industrial
copilot to not only identify problems
but present solutions.
As a result, fast and reliable maintenance
assistance will be ensured in the
event of a machine failure, minimizing
the downtime any unexpected problems
may cause. Additionally, getting access to
critical events and undigested shift data
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Connecting an industrial copilot to
the vast quantities of sensor data streaming
out of a factory then using that to
train the AI model on what is and is not
normal behavior allows it pick up on
minute changes preceding a failure and
bring that to the attention of maintenance
staff in a timely manner.
Through automated service instructions
and personalized recommendations,
a generative AI-powered industrial
copilot can also deliver tailored maintenance,
upgrades, and additional services
using extensive data collected from
across a factor or even a company as a
whole. This enables companies to boost
productivity, promote sustainability, and
expedite digital transformation by helping
break down the barriers between
people and information.
With the information age well underway
it is now all too easy to get lost in the
seas of data flowing in and out of everything
from smart appliances to cutting-edge
design tools. Generative AI is,
in many ways, a natural response to this,
helping make sense of the increasingly
complex and data-rich world and make
it accessible in a human way. Innovations
like Siemens Industrial Copilot will bring
that change into industry, changing forever
the way products are designed,
manufactured, and maintained.
This article was written Dr. Michael
" Michi " Lebacher, Head of AI and Digital
Businesses, Germany, Siemens AG,
Digital Industries. Erik Scepanski is an
Innovation Manager, Siemens Digital Industries
(Munich, Germany). For more
information, visit https://www.siemens.
com/global/en.html.
Tech Briefs, August 2024
https://www.siemens.com/global/en.html https://www.techbriefs.com
Tech Briefs Magazine - August 2024

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