PRESENTATION
1 - AVEVA PI World 2021 - Products
Increase Asset Reliability and Performance with No Code AI
Industrial assets often fail without any advanced notification causing unscheduled downtime, damage to equipment and increased safety risk. This unpredictability leads to substantial unexpected costs and profit loss.
In this presentation you ll learn how major industrial enterprises are reducing OPEX costs by 10-20% by transforming their operations and maintenance practices from reactive to predictive and prescriptive. Learn how you can leverage the industrial asset data in your PI System with Artificial Intelligence and Machine Learning to provide early warning detection, diagnosis and prescriptive remediation of industrial asset failures. Enable your workforce to focus on optimizing plant operations profitably and sustainably.
Key take-aways:
- Learn how to optimise existing investments and maximize productivity to boost margins
- Learn how Duke Energy has realized $100 s of millions in avoided costs over the last decade
Company
Duke Energy
Speaker
Tony File
Toni File has served in various Technical and Managerial roles in the field and corporate office since joining Duke Energy in 1986. Tony holds a BSME in Mechanical Engineering from the University of North Carolina Charlotte and manages the reliability program for Duke Energy's Fossil-Hydro Fleet that includes Duke Energy s fleet Monitoring and Diagnostic Center, and implementation of the fleet Equipment Reliability Program.
Company
AVEVA
Speaker
Sean Gregerson
Sean Gregerson, Head of Asset Performance Management Sales at AVEVA, has more than 20 years of experience applying advanced software technology solutions including Artificial Intelligence, machine learning, predictive analytics and IoT enabling industrial asset operators globally to improve the reliability and performance of their production assets. Gregerson is responsible for developing and executing the business strategy to ensure that AVEVA remains a leader in Asset Performance Management. He maintains a significant focus in helping companies achieve the greatest value from the rapidly increasing amount of industrial Big Data. Previously, Gregerson held control system design engineer and business development positions with a leading automation company. He received a Bachelor of Science in Electrical Engineering from Bradley University.