Outcomes, Insights and Best Practices from IIC Testbeds: Smart Factory Machine Learning for Predictive Maintenance Tesbed
customer engagements are currently being
explored. limiting results to a proof-of-concept or
applications in a controlled environment.
An important issue is that, currently, there
are many “expert systems” in production;
however, those systems have problems with
large false positive rates. In the testbed
team’s experience, almost all expert systems
of the type in focus experience shutdowns.
In a facility such as the OEM testing this
testbed solution, downtime costs USD
50,000 per hour, with unexpected failures
potentially taking 40 hours to address. In this
case, the testbed team’s approach is to test
specific algorithms that take into account
the dynamics of industrial systems, where
degradation occurs and normal limits are
understood (such as our cars where, five
years later, they are not as new but work
perfectly). These dynamic algorithms are
also mathematically supported, where the
designer (data scientist) is able to
understand what the algorithm is doing
internally (not black-box algorithms such as
neural networks, deep learning, etc.). All
these efforts help the predictive analytics to
minimize the false positives rate and save
significant costs.” E XPERIENCE
Aingura IIoT has derived business value
through the Smart Factory Machine Learning
Testbed by connecting with high-quality
vendors of specific technologies Aingura
needs to develop products. Finding vendors
with high-quality products to fulfill specific
technology needs is very difficult, but the IIC
ecosystem facilitates connections which
would otherwise be unlikely to form outside
the IIC. Not only has this helped Aingura
create new customers, but the testbed itself
has benefitted—for example, accessing a
testbed partner’s products for use in its
hardware platform. In addition, the testbed
can act as a marketing tool for partners to
showcase their products in real applications.
The partnerships within the testbed
expedited certain processes necessary to
build the testbed. The relationship with iVeia
and Xilinx, for example, enabled record-time
building of the new hardware platform with
the latest technology from each company.
Such a complex platform was expected to
take two or three years to create, but the
relationships shortened this process to only
six months.
One key takeaway learned by the testbed
team is that something is not finished when
only tested at a home facility. The team may
design
the
best
machine-learning
algorithms, best IoT hardware and best
architecture, but many changes take place
when going from a lab to a real industrial
environment. If not tested during real
production, there will be many false-
positives. For other testbeds and companies
considering an IIoT implementation, the
testbed team strongly suggests conducting
tests in real environments rather than
IIC Journal of Innovation
C LOSING
The most significant surprise throughout the
testbed’s journey has been the relationships
formed between partners. In particular, it
was unexpected how open to collaboration
each partner was and how an open
communication protocol was established.
When examining the testbed’s progress,
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