Title:

Systems Monitoring and Prognostics

Speaker:

Dr. Nagi Gebraeel

Affiliation:

Ga. Tech School of Industrial and Systems Engineering

When:

Monday, February 8, 2010 at 2:00:00 PM   Add to Calendar

Where:

Van Leer Building Building, Room 218

Host:

Dr. Karim Sabra
karim.sabra@me.gatech.edu
404-385-6193

Abstract

Recent advances in sensor technology and wireless communication have enabled monitoring of complex engineering systems. Research on interpreting and exploiting these rich data streams in making critical decisions stands to provide significant advancements across a wide range of application domains. One important application is predicting failure and performance degradation of engineering systems, which I define as Systems Monitoring and Prognostics (SMP). This talk is geared towards presenting the key components of the SMP framework; (1) acquisition and analysis of sensor data, (2) development of stochastic/statistical prognostic degradation models, and (3) development of adaptive prognostics-based operational and logistical decision models. I will focus on the development of a degradation-based prognostic methodology that utilizes sensor-based condition/health monitoring information to compute and continuously update, in real-time, residual life distributions of partially degraded systems. This methodology rests on the idea that the evolutionary paths of the degradation signals can be modeled as a continuous-time continuous-state stochastic process whose trajectory is revised using in-situ signal observations. Under this construct, the residual life distribution becomes the distribution of the time it takes the amplitude of a degradation signal to reach a pre-specified failure threshold. I will also demonstrate how sensory-updated residual life distributions can be integrated with basic replacement and spare parts inventory models to create an autonomic “Sense and Respond” logistical paradigm.

Biography

Dr. Nagi Gebraeel is an assistant professor of Industrial and Systems Engineering at Georgia Institute of Technology. He received his MS and PhD in Industrial Engineering from Purdue University, 1998 and 2003 respectively, and his B.Sc. in Production Engineering from the University of Alexandria, Egypt, in 1995. Professor Gebraeel’s research interest is in leveraging sensor-based data streams to improve the predictability of unexpected failure of engineering systems and advance subsequent operational and logistical decision making processes. He is a member of the Institute of Industrial Engineering (IIE) and the Institute of Operations Research and Management Science (INFORMS).