University of Vermont

Structural Monitoring Diagnostics and Prognostics Lab

K-12 Education

CE 395-Reliability of Engineering Systems


3 credits

Safe and reliable performance of engineering systems under a variety of uncertain and extreme working conditions is of enormous importance to society, especially considering the consequences associated with such failures. Nuclear power plants, bridges, aircrafts, power grids and computer networks, just to name a few, are examples of such systems. Many of these systems operate interdependently, cut across disciplinary boundaries and require advanced computational methods to evaluate their performance. As engineers we are interested not only in computing their response under assumed conditions, but more importantly, in modeling and quantifying the uncertainty associated with their performance. Our final goal is to use scientifically based predictions to make informed decisions related to design and operational safety.

The two main themes of the course will be: 1) Uncertainty modeling and quantification associated with computer-based simulations of engineering systems, and 2) Reliability of existing systems based on performance monitoring and damage models (Bayesian approach).

References:

Probabilistic Risk Assessment of Engineering Systems. M.G. Stewart and R.E. Melchers. Chapman and Hall, 1997

Reliability and Risk – A Bayesian Perspective.i> N. Singpurwalla. John Wiley and Sons, 2006

Methods of Structural Safety.i H.O. Madsen, S. Krenk and N.C. Lind. Dover, 2006

Structural Reliability Analysis and Prediction, 2nd Edition by R. Melchers. John Wiley and Sons, 1999

Probability and Random Processes for Electrical Engineers 2nd Edition by A. Leon- Garcia. Addison-Wesley, 1994



Stochastic Systems. M. Grigoriu. Springer-Verlag, 2012 Random Fields. E. Vanmarke. MIT Press, 1983

Prerequisites by topic:

≥ STAT-151 (or equivalent)

Instructor:

Dr. Eric M. Hernandez Office: 217 Votey Hall
E-mail: eric.hernandez@uvm.edu

Objectives:

1. Review fundamental theory of probability – emphasis on measure theory
2. Provide tools for modeling and simulating random variables and random processes
3. Provide numerical methods for modeling and propagating uncertainties in engineering systems.
4. Provide methods to compute the reliability of engineering systems and components
5. State-of-the-art methods to update system reliability based on operational data

Topics:

  • Introductory discussion: Systems, models, uncertainty, failure and risk
  • Foundations of probability theory-emphasis on measure theory
  • Random variables
  • Functions of random variables
  • Random processes
  • Random fields
  • Component reliability analysis
  • System reliability analysis
  • Second moment methods
  • Stochastic simulation methods
  • Analysis of failure data
  • Bayesian reliability analysis based on operational data

Computer Usage:

MATLAB

Grading:

Assignments 20%
Final Research Project 30%
Midterm 25%
Final 25%

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Last modified December 19 2013 06:16 PM