Predictive Intelligence Platform for Asset Integrity Management

Speaker Dr. Alexander G. Parlos
Mr. Tommy Knight and Dr. Alexander G. Parlos founded Veros Systems in College Station, Texas.  Dr. Parlos is a Professor of Mechanical Engineering at Texas A&M University, with joint appointments in the Department of Nuclear Engineering, and, by courtesy, Department of Electrical and Computer Engineering. He has established and is ...

Mr. Tommy Knight and Dr. Alexander G. Parlos founded Veros Systems in College Station, Texas.  Dr. Parlos is a Professor of Mechanical Engineering at Texas A&M University, with joint appointments in the Department of Nuclear Engineering, and, by courtesy, Department of Electrical and Computer Engineering. He has established and is heading the Networked and Intelligent Machines Laboratory (NIML) where graduate and undergraduate students, and postdoctoral visitors conduct research on non-invasive and non-intrusive interfaces for machine monitoring and related applications utilizing standard IP networks for communication, and on adaptive methods rooted in machine learning, pattern recognition and more traditional control engineering approaches. His applied research interests include the development of data-driven approaches for condition and performance assessment of various dynamic systems. He has been involved with the particular application of these concepts to electro-mechanical and mechanical systems, and more recently to distributed real-time computer systems.  His research is sponsored by various federal
agencies (NSF, US Department of Energy, US Department of Defense, NASA), State of Texas agencies, industry groups (EPRI, APPA), as well as private companies. Dr. Parlos serves on several Editorial Boards of research journals and on various national and international technical committees and review panels for
federal funding agencies. He is a Fellow of the ASME and a licensed professional engineer in The State of Texas

Full Description
This presentation will seek to introduce a technology solution that provides a continuous, non-intrusive,
scalable, predictive monitoring tool for electrically driven assets and entire power trains. The Predictive Intelligence Platform (PIP) tracks and analyzes small and slowly changing variations in the distortion levels of the monitored electrical signals, the three phase voltages and currents. The software distinguishes sources of waveform distortion, whether caused by changes in incoming grid power, driven process or asset condition and identifies each of the sources.  The software continuously acquires electrical waveforms at the motor switches with high sampling rates. No sensors are placed on the monitored machines. The software continuously
analyzes and interprets these waveforms for impending mechanical or electrical faults and energy efficiency assessment. Impending faults and related details are provided through a web-based graphical interface and other standard interfaces providing a seamless integration with leading Enterprise Asset Management suites.
 
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When?

Tue, Jun. 18, 2013
11:30 a.m. - 1 p.m. America/Chicago

How Much?

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Where?

Norris Center City Center
801 Town & Country Blvd #210
Houston, TX 77024
USA

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