Energy Systems Engineering Seminar Series: "Anomaly Detection in Operating Energy Assets Using Advanced Pattern Recognition"

Tuesday, March 9, 2021 at 12:00pm to 1:30pm

Virtual Event

Register today for the 3rd installment of Energy Systems Engineering Spring 2021 Seminar Series.
This seminar is available only online via Zoom.

"Anomaly Detection in Operating Energy Assets Using Advanced Pattern Recognition" 

Presented by: Kenneth Gross of Oracle

REGISTER HERE

DATE: Tuesday, March 9, 2021
TIME: 12:00 PM Eastern Standard Time
LOCATION - ZOOM MEETING link to be sent prior to the seminar date
CONTACT US: inesei@lehigh.edu  

Abstract - 

The Multivariate State Estimation Technique (MSET) is an advanced prognostic pattern recognition method that was originally developed by Argonne National Laboratory (ANL) for high-sensitivity prognostic fault monitoring applications in commercial nuclear power and aerospace applications.  MSET has since been spun off and met with commercial success for prognostic machine-learning (ML) applications in a broad range of safety critical, mission critical, and business applications, including NASA space shuttles, military gas turbine and ship-propulsion prognostics, Oil-and-Gas exploration and refinery predictive and prescriptive maintenance, human-in-the-loop supervisory control, prognostic cyber security for SCADA assets and networks, as well as Utility distribution grid and renewable asset prognostics.

 

Over the last 20 years, Oracle has pioneered a suite of intelligent-data-preprocessing (IDP) algorithms and automated tuning and sensitivity-optimization algorithms for a second generation MSET called MSET2.   MSET2 possesses significant advantages over conventional ML algorithmic approaches, including neural networks, autoassociative kernel regression, and support vector machines.  MSET2 advantages: higher prognostic accuracy, earlier warning of incipient anomalies in complex/dynamic/chaotic time-series signatures, lower false-alarm and missed-alarm probabilities (Type-I and -II error rates), and much lower overhead compute cost, which is crucial for real-time dense-sensor streaming prognostics.  Each of these advantages for MSET2 will be demonstrated during the presentation for several challenging Energy Industry use cases. Come join us for this seminar!

Bio - Kenny Gross is an AI Solutions Architect with Oracle's Physical Sciences Research Center in San Diego.  Kenny specializes in prognostic machine learning (ML) and autonomous data-science innovations for improving the reliability, availability, energy efficiency, and security of enterprise computing systems and networks, and for real-time prognostics for dense-sensor Internet-of-Things applications in IoT industries that include Utilities, Oil&Gas, Renewables, prognostic cyber security, commercial aviation, autonomous vehicles, and Industry 4.0 smart manufacturing.  Kenny has 288 US patents issued and pending, 224 scientific publications, and was co-recipient of a 1998 R&D 100 Award for one of the top 100 technological innovations in the world for that year, for an advanced statistical pattern recognition technique (MSET) that is now finding wide uses in industrial and medical applications.  Kenny joined Oracle in 2000, and for the last 20 years has led the development and productization of pioneering MSET2-related innovations in Oracle's advanced ML pattern recognition portfolio.

If interested, please attend the presentation on Tues, March 9, at 12:00pm Eastern Standard Time

For more information about the Energy Systems Engineering Master's degree program, visit our website:  ese.lehigh.edu

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