Outcomes, Insights and Best Practices from IIC Testbeds: Smart Factory Machine Learning for Predictive Maintenance Testbed
IISF from the cybersecurity point of view.
published a book, Industrial Applications of
Machine Learning, in late 2018 which
features two chapters that discuss the
testbed’s results in the Aingura lab.
Time Sensitive Network (TSN) standards, as
related to the IIC TSN Testbed, play a role in
the implementation of the Smart Factory
Machine Learning Testbed. Between
decision-making, processing and pre-
processing, there is communication—the
transportation of data from one place to
another. The deterministic approach of TSN
increases the security of the data during
communication, helping to avoid the loss of
that data. In the Smart Factory Machine
Learning Testbed, the gathered data needs
to feed into the machine learning
algorithm—TSN protects the data during this
communication.
The deployment of the testbed’s
technologies in the controlled lab
environment is fairly straightforward, but
deploying in the real industrial environment
involves several challenges. In a real
production facility, the window of time
available to deploy the technology is
drastically limited due to the production
schedule of the facility. As a machine is
producing, it cannot be stopped or tampered
with for the sake of running tests. When that
short window of time opens, the system is
connected and deployed, but to check if it is
working properly may take several months
and is thus not feasible. This differs from
deploying a brownfield system which can be
deployed in existing production lines. In a
scenario where production machines are
built in the Aingura lab and sent to the end
user with the technology already installed,
proper validation tests can be implemented.
However, it is not common that a machine is
installed in a new production line—so the
opportunities to deploy the testbed’s
technology are limited.
T ESTBED R ESULTS
The testbed’s first deliverable was published
in the IEEE Internet of Things Journal in May
2018, covering the results of one of the
testbed’s first dynamic machine learning
algorithms which works with the data
stream coming from a machine. At this time,
the testbed team is working on a new article
for the Engineering Applications of Artificial
Intelligence which is aimed to be published
by early 2020. This paper discusses machine
learning algorithms oriented to predictive
maintenance. Next, the testbed team was
granted a U.S. patent for their architecture
that increases computing power by
connecting several aspects of Aingura’s
hardware platform to deploy the testbed’s
solutions. The hardware integration gives
more computing power at the edge and
allows for more complex machine learning
algorithms to be deployed in real industrial
environments. Finally, the testbed team
To bypass this challenge, Aingura’s parent
company Etxe-Tar, which has developed
strong relationships with OEMs for the last
20 years, is able to provide a channel for the
testbed’s technology to find its way into
production facilities. Without a strong
relationship with an end customer’s
production department, this is far more
difficult. The testbed team seeks to find
other ways to implement the testbed in real
production environments, and some
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