SIE 408 - Reliability Engineering

SIE 408 - Reliability Engineering (3) Time-to-failure, failure-rate, and reliability determination for early, useful and wear-out lives; equipment reliability predictions; spare parts provisioning; reliability predictions; spare parts provisioning; reliability growth; reliability allocation. Credit for this course or A ME 472. P, 330 or A ME 474, MATH 223. 1.5ES, 1.5ED. Graduate-level requirements include a special report of 30 pages on a specific reliability engineering topic. Credit for this course or A ME 572.

Elsayed A. Elsayed, Reliability Engineering .

Class notes available at cost.

References: None

Duane Dietrich, Professor of Systems and Industrial Engineering

1. Engineering statistics or the equivalent
2. One university level introductory course in probability and statistics within the last five years is highly recommended or a review course in statistics and calculus prior to taking SIE 408.

Short review followed by two comprehensive homework sets

A goal of this course is to provide students with a broad theoretical background in the modeling and analysis of reliability systems. a second goal is to prepare students to read and understand the reliability literature. This is an application oriented course.

1. Understanding of concept of component failure rate.
2. Understanding of basic component time to failure statistical models.
3. Ability to predict systems reliability from component failure data.
4. Ability to predict systems availability from component failure data and repair process data

1. Introductory Concepts (6 hours)
2. Discrete Probability Models of Reliability Engineering Importance (3)
3. Probability Density Functions used to Calculate Reliability (3)
4. Mixed Distribution and Combined Effects Models (3)
5. Complex Systems Design and Analysis (6)
6. Optimal Design Methods for Systems (3)
7. Distributions of Sample Statistics and Their use in Constructing Confidence Intervals on Reliabilities (6)
8. Point an Interval Estimates for the Exponential (3)
9. Bayesian Point and Interval Estimates for Exponential Binomial, Geometric and Poisson Distribution (3)
10. Markov Models for Repairable Systems Reliability (3)
11. Markov Models for Repairable systems Availability (3)
12. Renewal Theory (3)

Class Requirements:

1. Three lecture sessions per week.
2. 11 comprehensive homework assignments per semester
3. Two examinations and a final

1. Student must use spreadsheets for homework assignments
2. Knowledge of a standard statistical package is desireable.

Laboratory Projects: None

1. Graded examinations.
2. Graded homework sets

Contribution to program objectives: Goals 1, 2, 4, 5

Prepared by: Duane Dietrich    Date: April 30, 1998