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SasS Machine Learning Platform

The company releases the "first" machine learning platform for maintenance and plant automation systems with turnkey integration.

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With a healthy skepticism out there about cloud applications and plant manufacturing playing together, a new system, called mVision, uses a combination of supervised and unsupervised learning techniques to determine predictors of equipment failure and then continuously monitors it.

Developed by team of data-mining specialists, the platform includes pre-built adapters for maintenance, automation and condition monitoring systems, which converts all data into the Machinery Information Management Open Systems Alliance (MIMOSA) open-standard model. With the open standards, the system can integrated to MES and ERP packages for scheduling decisions based on capability forecast.

Alex Bates, Chief Technology Officer, Mtelligence, says,"Machine learning hasn't hit mainstream in manufacturing, in part due to the effort required to build an accurate model and get the data needed to train the system."

Other features of the platform are a library of intelligent processing filters for sensor data, including statistical process control and signal processing algorithms to improve the signal-to-noise ratio prior to training the platform. That feature offers the platform to benchmark a normal operating context and use it to compare "abnormal" operating conditions.

The system also has the ability to correlate sensor data with equipment assembly and transactional date coming from EAM/CMMS systems, and it includes prebuilt adapters for SAP, IBM, Maximo, Infor EAM, Infor Hansen, Ventyx EMPAC, JD Edwards and others. For operations, drivers are provided for plant historians, including OSIsoft PI System, Wonderware Historian, GE Proficy, Honeywell PHD and numerous other automation packages.

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