Tutorial on Fault Diagnostics/Prognostics for Equipment Reliability and Health Maintenance

Date: 13 Jan 2005 - 13 Jan 2005

Venue: SIMTech, Training Room 2, Tower Block, Level 3


This intensive one-day course is offered by Prof George Vachtsevanos of the School of Electrical and Computer Engineering, Georgia Institute of Technology, USA, in collaboration with SIMTech, Singapore. The course is intended to introduce practitioners and researchers in the reliability area to novel concepts and methodologies for machine condition-based maintenance that have been shown to perform reliably and robustly in actual dynamic systems. The industrial, marine and military communities are concerned about critical system/component reliability and availability. They are seeking to maximise equipment uptime while minimising costs. This course will focus upon an integrated hardware / software approach to machine health maintenance by introducing a systematic framework to failure mode and effects criticality analysis, and means to diagnose machine/component impending failure conditions and to prognose their remaining useful lifetime.

About the Speaker

Prof George Vachtsevanos is a professor of Electrical and Computer Engineering at the Georgia Institute of Technology. He directs the Intelligent Control Systems laboratory where faculty and students are conducting research in intelligent control, sensors and sensing strategies, neuro-fuzzy systems and diagnostics/prognostics for complex dynamic systems. He has developed and taught short courses in neuro-fuzzy control and diagnostics/prognostics for health management of aerospace systems. Prof Vachtsevanos is directing research in information technologies and fault-tolerant control of autonomous vehicles under DARPA sponsorship and the application of diagnostic/prognostic algorithms on shipboard critical systems for the U.S. Navy. His group is conducting CBM/PHM-related research for DARPA, ONR, and the U.S. Air Force Space Command, and other agencies. He serves as a consultant to General Dynamics for the AAAV PHM programme.