Tech Briefs Magazine - March 2023 - 20

Predictive Maintenance
management system on every robotic
welding gun. Unplanned downtime fell
by 25 percent.
At a silicon wafer plant, wafers are
sawed from silicon cylinders in a process
that takes more than eight hours. Poor
cuts impact quality and lead to high rejection
rates. The AI software mapped
healthy cutting data utilizing more than
40 system parameters, environmental
data such as humidity, and geometrical
data. Anomalies led to early detection of
defects. AI-derived maps were used to
improve accuracy in quality assessment
sampling. These led to a higher level of
identification of true defects during
quality observation. The wafer plant
now saves up to $100,000 per machine
per year through reductions in late defect
detection.
This energy savings application for ice
cream production differs from the previous
examples because the data necessary
to measure pneumatic air consumption,
which was the goal of the project, was not
available on the machine. To remedy the
situation, the plant added hardware to
digitally collect air pressure and flow data.
The plant also installed additional sensors
to increase data points for predictive
maintenance. The AI solution monitored
six pneumatic cylinders for predictive
maintenance and monitored compressed
air consumption for predictive energy savings.
Anecdotal information indicates improved
energy efficiency and uptime.
Adding hardware and sensors to an existing
machine enabled an AI-based asset
management system.
Identification of Anomalies,
Now What?
Early in the ML/AI development process,
the idea was that all the data would
be pushed into the cloud and displayed
on vendor-created dashboards. The solution
seemed reasonable - a direct line
from machine to the cloud for anytime/
anywhere monitoring. The hurdle was
that customers had their own dashboards
and own way of displaying data.
It became clear that customers wanted
the AI healthy/not healthy data available
in the manner most convenient for them,
whether on their own dashboards, resident
on the premises, on edge, or all three.
Some customers wanted the data integrated
with their maintenance management
systems. Others wanted alerts sent to mobile
devices. Many asked whether they
could have easily understandable messages
identifying what the problem was, where it
was, and what corrective action should be
taken. These requests led developers to understand
that the system must support the
end user's workflow or the OEM's HMI.
To be successful, as mentioned earlier,
these systems must be vendor agnostic
and must give the operations the ability
to decide where the software platform
can live directly on the system (on edge),
on premises servers, or in the cloud. It
must be flexible enough to enable connections
to internal maintenance management
software or spare parts management
system to create an integrated
end-to-end solution.
AI may appear futuristic, but this solution
has advanced to the point where it is
experienced by the user as a practical tool
for improving the bottom line.
This article was written by Frank Latino,
Product Management Electric Automation,
Festo North America (Islandia,
NY). For more information, visit www.
festo.com/us/en/.
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Tech Briefs, March 2023
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Tech Briefs Magazine - March 2023

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