Outcomes, Insights and Best Practices from IIC Testbeds: Smart Factory Machine Learning for Predictive Maintenance Tesbed
To extend the usefulness of the published testbeds in the Testbed Program of the Industrial
Internet Consortium (IIC), the Testbed Working Group has developed an initiative to interview
the contributors of selected testbeds to showcase more insights about the testbed, including the
lessons learned through the testbed development process. This initiative enables the IIC to share
more insights and inspire more members to engage in the Testbed Program.
This article highlights the Smart Factory Machine Learning for Predictive Maintenance Testbed.
The information and insights described in the following article were captured through an
interview conducted by Mr. Howard Kradjel, Vice President of Industry Programs at IIC, with Dr.
Javier Díaz, Chief Technology Officer of Aingura IIoT. Javier is an active member in the IIC where
he serves as a co-lead of the Smart Factory Machine Learning Testbed and is a key contributor to
the Testbed Working Group.
S MART F ACTORY M ACHINE L EARNING FOR P REDICTIVE M AINTENANCE T ESTBED –
F ROM C ONCEPT TO R EALITY
the quality of the data coming from the
industrial environments (i.e., in terms of
noise reduction) in order to get the right
quality of data to perform the advanced
analytics. The transportation of data from
one place to another, the storage of that
data and the implementation of these new
machine
learning
algorithms
must
contribute to forming actionable insights for
the end user. The actionable insights depend
on the question or problem the end users
are trying to solve. Related to predictive
maintenance, the questions are related to
the degradation level of a specific part of the
machine, cell or line that could fail stopping
the production, i.e., increasing downtime or
long mean time between failures (MTBF),
etc. Therefore, the output of the algorithms
or the machine learning system is to tell the
end user the remaining useful life (RUL) of
that particular element. For example, a first
use case in the testbed is working to predict
the RUL of the frontal ball-bearing of a
spindle head. Specifically, the actionable
insights given to the end user is the % of RUL,
Founded in 2017, the Smart Factory Machine
Learning for Predictive Maintenance
Testbed seeks to test algorithms and
architecture solutions in the form of
technologies: communication protocols,
cloud platforms, cybersecurity, etc. to
achieve predictive maintenance. Many
existing machine learning technologies can
be applied to predictive maintenance, but
most are not fully developed to optimize the
accuracy or performance of those elements
in order to retrieve actionable insights.
Many are able to detect a failure, but they
do not have the ability to perform the
specific activity in real, industrial
environments. The testbed’s goal is to
develop algorithms and test them in IIoT
architectures
and
real
industrial
environments.
Some experimentations lead to the
development of dynamic algorithms—
machine learning algorithms that are not
necessarily well expressed in state-of-the-art
industrial environments but that guarantee
IIC Journal of Innovation
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