Industrial Internet Consortium
Industrial Internet Consortium Smart Factory Machine Learning for Predictive Maintenance Testbed Enters Phase 3 with Commercial Deployment
Two-year old testbed increases factory uptime and asset efficiency
NEEDHAM, MA – October 8, 2019 – The Industrial Internet Consortium® (IIC™) today announced that its Smart Factory Machine Learning for Predictive Maintenance Testbed has successfully completed phases 1 and 2 is entering into phase 3 with a commercial deployment. This testbed is deployed at a tier one automotive OEM, was granted two US patents, was featured in a recent book and profiled in two peer-reviewed technical journals.
IIC members Aingura IIoT and Xilinx developed the testbed to evaluate and validate machine learning techniques for predictive maintenance of high-volume production machinery. The testbed has been successfully deployed to production environments at machining and automotive OEM manufacturing facilities. Machine learning techniques enable these facilities with high-volume machinery to move away from regularly scheduled preventative maintenance to predictive maintenance for optimized system operation and asset utilization.
“Today’s methodology of preventative maintenance is not cost efficient and doesn’t address problems that lead to system failure,” said Dan Isaacs, IIC Testbed Lead, and Director of Customer Marketing, Xilinx. “With predictive maintenance, operators can be alerted before system failure, and in some cases without operator interaction, avoiding costly unplanned downtime and improving energy efficiency. With the Zynq UltraScale+ MPSoC at the heart of Aingura IIoT’s system Aingura Insights, both sensor hub, Time Sensitive Networking (TSN) and machine learning functionality are performed efficiently and effectively, all within a single device.”
“This testbed focuses on exploring the application of machine learning techniques and algorithmic approaches using new innovative technology,” said Javier Díaz, IIC Testbed Lead, and CTO at Aingura IIoT. “With proper analysis, the information can provide a wealth of insight into a company’s system operations, overall operating and maintenance costs. Furthermore, companies can pass the savings they achieve during product manufacturing on to their customers.”
The testbed team was granted two US Patents US20180253084A1, US10031500 for “Device and system including multiple devices for supervision and control of machines in industrial installation.” The testbed is mentioned in a book entitled, “Industrial Applications of Machine Learning,” of which Diaz is an author. The testbed results are also published in the Journal of Innovation, “Making Factories Smarter Through Machine Learning,” and IEEE Journal, “Clustering of Data Streams with Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes,” May 24, 2018.
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