This courses starts with basic definitions used in Reliability and Quality Engineering. Basic probability concepts are introduced and used to calculate the reliability of series and parallel systems from component data. Decomposition and Event Space methods are used to calculate the reliability of complex systems. Basic statistical concepts, such as Random Variables, Probability Density Functions, Cumulative Distribution Functions and Expectation, are covered. Examples from Statistical Process Control and Reliability are used to emphasize the practical importance of discrete statistical distributions such as the Binomial and Poisson. Continuous distributions, such as the exponential, Weibull, normal and lognormal are demonstrated as time-to-failure models for early life, useful life and wear-out. Concepts of random sampling and sampling statistics are covered. Distribution parameters are estimated from data and point and interval reliability predictions are obtained. Basic concepts in the design of reliability tests are covered and practical examples of model parameter estimation and reliability prediction using data obtained from various types of censoring and grouping schemes are included.

This course requires 24 contact hours. It is offered as three-day course, and it is designed as a University style course.