Maximize Historian Data to Avoid Costly Equipment Failures with Guided Asset Analytics

Learn to break down barriers and make machine learning accessible to your whole team through a user-friendly and adaptive UI that leverages common visualization and analysis tools.

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Performance

Break down the barriers to machine learning to accelerate output and optimize performance.

Because failure is never an option, asset analytics are one of the most useful technologies that can continually deliver tangible business value from your Historian data. However, machine learning has remained complex and elusive for common-purpose applications such as pumps, motors and drives.

This webinar will show how to break down barriers and make machine learning accessible to your whole team through a user-friendly and adaptive UI that leverages common visualization and analysis tools.

Watch the webinar to learn how existing historian data can be used to model optimal equipment performance and highlight critical data in real-time to identify anomalies and mitigate issues before they become critical. A simple cloud-based strategy will be presented that shows how to drive an action such as an alert, workflow or work order based off of performance anomalies and historical data.

This webinar will demonstrate how to easily leverage failure modes, predictive models, asset types, remediation maintenance plans, fault diagnostics, through a prebuilt cloud-based library.

Learn how to:

  • Remove time limitations from your data
  • Gain a complete picture of operational performance
  • Easily discover and share trends and patterns in operational data
  • Visualize alarm data together with process data to infer patterns
  • Access machine learning without coding

 

Duration: 60 minutes

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Speaker:

Justin Thomas

Senior Director, Global Channel APM Sales at AVEVA

Justin is leading the Enterprise Asset Performance Management portfolio globally. He has over 16 years of experience providing technology solutions to industrial companies across the world and is Schneider Electric’s predictive asset health monitoring expert. His professional experience includes international business development, account management and industry consulting. Justin earned a Bachelor of Science in Mechanical Engineering from Lafayette College and a Master of Business Administration from DePaul University.

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