Attacking Unplanned Downtime Through Predictive MaintenanceIsaac Maw
posted on September 25, 2017 |
A new Industrial Internet Consortium testbed aims to tackle
the inefficiency of scheduled preventative maintenance. With the real-time data
flow of IIoT-enabled
machines and components, predictive
maintenance has the potential to make preventative maintenance obsolete.
What is the Industrial Internet Consortium?
Internet Consortium (IIC) was founded by AT&T,
Electric, IBM and Intel in 2014. The IIC is a non-profit
organization working to set standards, define best practices and accelerate the
adoption of IIoT technology. Their testbed program enables real-world
manufacturers to test, research and tinker with industrial internet applications.
The resulting case studies and data helps bring those applications to market
faster. Examples of IIC testbeds include smart airline baggage management,
connected vehicle urban traffic management and connected medical care.
Stay up and running: downtime costs some manufacturers as much as $22k per minute.
Smart Factory Machine Learning for Predictive Maintenance
The testbed is led by two companies that specialize in Industry
4.0 solutions: Plethora IIot and
electronics company Xilinx. The main goal
of the testbed is to evaluate machine learning techniques for predictive
maintenance on real-world, high-volume production machinery.
The challenge is to detect and understand failures, to
ultimately reduce downtime and increase energy efficiency. IIoT sensors can
gather enormous amounts of real-time data, but critical analysis is required to
maximize the usefulness of that data.
learning is the solution to this problem. AI can identify, monitor and
analyze system variables during operation, then alert operators before a system
failure occurs. Ideally, this could eliminate costly unplanned downtime.
“Downtime costs some manufacturers as much as $22k per
minute. Therefore, unexpected failures are one of the main players in
maintenance costs because of their negative impact due to reactive and
unplanned maintenance action. Being able to predict system degradation before
failure has a strong positive impact on machine availability: increasing
productivity and decreasing downtime, breakdowns and maintenance costs,” said
Plethora IIoT team leader Javier Diaz.
“We’re excited to lead this testbed with Xilinx and work
alongside some of the leading players in IIoT technologies. This is a unique
opportunity to test together machine learning technologies with those involved
in the testbed at different development levels starting from the lab through
production environments, where a real deployment solution is utilized. As a
result, from these experiences, we can significantly reduce the time-to-market
of Plethora IIoT solutions oriented to maximize smart factory competitiveness,”
First, the plan is to develop the machine learning
techniques and algorithmic approaches in a lab setting in Spain, then try it
out in a controlled production environment. Ultimately, the final test will be
in deployment at an automotive OEM manufacturing facility.
Predictive maintenance has the potential to add significant
value to production processes by increasing efficiency and reducing unplanned
and redundant costs. Additionally, the capability for better analysis of IIoT
data makes IIoT devices more valuable, as more and more uses for the data are
To learn more about the Industrial Internet Consortium’s
Testbed Program, click here.
For more information about the Industrial Internet of Things, click here.