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Taking Predictive Maintenance into Analytics

IBM Watson, predictive maintenance
Grabbing data from plant equipment has become a relatively straightforward IoT deployment. Analyzing that data is more complex, but it delivers significant benefits.

Predictive maintenance delivers measurable benefits in its ability to reveal equipment weakness before failure. Analyzing predictive maintenance data can deliver even greater benefits in the ability to optimize plant equipment and processes. It can offer pathways to KPI improvements. Analytics can also determine what solutions are required when equipment begins to fail.

Predictive maintenance analytics can be used to optimize plant processes. (Image source: IBM Watson)

At the Atlantic Design and Manufacturing Show in New York City last month, Tom Craven, VP of product strategy at RRAMAC Connected Systems, and Kayed Almasarweh, Watson and cognitive IoT solutions lead at IBM, explained both the process of gathering plant data and how the analysis of that data can improve plant systems.

The one-two punch of Craven and Almasarweh offered a strong program that explained the possibilities of predictive maintenance when its data is pushed into analytics. Craven has long been one of Design News’ top sources—both in feature stories and as a speaker at conferences. Almasarweh brings the additional perspective of IBM Watson’s analytics. IBM has long been ranked among the top companies in predictive maintenance analytics.

This session, Leveraging IoT for Predictive Maintenance, draws on the strengths of both experts. It covers the benefits of tracking equipment via IoT data gathering and analyzing the resulting data in order to optimize plant systems.

Rob Spiegel has covered automation and control for 17 years, 15 of them for Design News. Other topics he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.

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